Systems doi: 10.3390/systems14070739

Authors: Ayokunle Olagoke Gloria Aidoo-Frimpong Comfort T. Adebayo James Shaw Oluwatobi Adegbile Ayomide Owoyemi Ziwei Qi Hayrettin Okut

Rural communities face persistent challenges in accessing timely, culturally relevant, and trustworthy health information due to inadequate communication infrastructures, workforce shortages, and infrastructural constraints. As generative artificial intelligence (GenAI) tools become increasingly accessible, rural serving organizations are often pushed to explore their use to expand communication reach and reduce staff burden through funding incentives, vendor offerings, and policy signals, even when adoption is misaligned with local capacity or priorities. However, guidance is lacking on how rural systems should approach GenAI adoption in ways that strengthen, rather than undermine, trust and equity. This Opinion offers a systems-oriented and community-centered perspective on rural GenAI readiness by distinguishing between non-agentic applications that support human communicators and agentic systems that introduce varying degrees of autonomy. We propose a staged maturity framework tailored to rural health communication ecosystems, outlining opportunities, risks, and governance needs at each stage of adoption. By centering on rural context, communication trust, and system readiness, this Opinion aims to support the intentional, ethical, and sustainable integration of GenAI into rural health communication systems.

Systems doi: 10.3390/systems14070738

Authors: Lucia Duricova Veronika Labosova

In bank-dominated financial systems, the accumulation of non-performing loans is a recognised source of systemic vulnerability, as correlated corporate defaults can erode bank capital, impair liquidity, and propagate stress across interconnected portfolios. Firm-level default detection thus constitutes a microprudential foundation of macroprudential stability: the reliable early identification of risky borrowers reduces both individual credit losses and the aggregate exposures that drive system-level fragility. Yet the use of structured data-mining pipelines for this task remains underexplored in Central and Eastern Europe. This study applies the CRISP-DM methodology to predict corporate loan default using data on 302 Slovak corporate borrowers, combining financial ratios from publicly available financial statements with selected company and loan-related information from internal bank records. Seven individual classifiers were developed and compared: decision trees (CART, CHAID, C5.0), logistic regression, discriminant analysis, and neural networks (MLP, RBF), together with a stacked ensemble based on their outputs. Model performance was evaluated using sensitivity, overall classification accuracy, and area under the ROC curve (AUC), with sensitivity treated as the primary criterion because of the asymmetric costs of misclassification in credit risk assessment. The results confirm that historical firm-level information provides a reliable basis for default prediction, with tree-based models consistently outperforming statistical and neural network approaches. The stacked ensemble achieved the strongest overall performance, whereas C5.0 and CHAID showed that interpretable classifiers can also deliver competitive predictive accuracy. A champion–challenger deployment architecture is proposed, in which the ensemble serves as the performance-oriented champion and interpretable models act as challengers; this arrangement contributes to the operational resilience of the credit-risk assessment process and aligns with macroprudential expectations of model governance, auditability, and explainability. The study offers a replicable methodological framework for integrating data-driven decision support into credit evaluation in comparable banking settings.

Systems doi: 10.3390/systems14070737

Authors: Cheng Chen Jinsheng Zhu Haixia Sun

This study investigates the complex and nonlinear interaction between the dual circulation paradigm and high-quality full employment. Moving beyond the limitations of conventional static partial equilibrium frameworks, the analysis conceptualizes this relationship as a system of three interrelated feedback loops. Drawing on system dynamics (SD) theory, a set of nonlinear differential equations is developed, with model parameters calibrated using macroeconomic data from 2010 to 2025. The simulation results yield three main findings. First, international trade, cross-border investment, and technological exchange jointly form a core reinforcing feedback loop that underpins the mutually beneficial interaction between domestic and international circulations. Second, the integrated development of education, technology, and human capital emerges as a critical state variable for overcoming the persistent trade-off between employment quantity and quality. Third, the interplay between horizontal market expansion and vertical technological advancement constitutes a dual driving mechanism that facilitates the system’s transition toward a higher-level equilibrium, with multi-factor interactions generating pronounced nonlinear multiplier effects. Overall, the study provides a quantitative basis for designing adaptive and targeted employment policies within the dual circulation framework.

Systems doi: 10.3390/systems14070736

Authors: JooHyun Park Keun Tae Cho

Is research funding shaping innovation in the global AI competition? How research funding translates into innovation outcomes in the global artificial intelligence (AI) race remains insufficiently understood. Prior studies have largely focused on input–output relationships, providing limited insight into the structural mechanisms through which funding shapes innovation performance. This study examines whether research funding is associated with innovation through differences in collaboration structures and knowledge diversity within AI research ecosystems. Using an observed-variable path model estimated as a system of seemingly unrelated regressions (SUR), together with multi-group analysis, applied to 98,241 AI-related publications indexed in the Web of Science from 2011 to 2024, the study analyzes relationships among funding, structural change, and innovation outcomes across major national innovation systems. The results suggest that research funding is associated with higher research productivity and impact, partly through expanded collaborative networks. Funding appears modestly linked to greater thematic diversity, though this association is not robust across specifications, while interdisciplinary exploration tends to correspond with weaker short-term citation performance, suggesting a potential delay in the recognition of novel knowledge combinations. In addition, the extent to which funding translates into outcomes appears to vary across countries. These findings suggest that funding may be associated with AI innovation not only through greater research capacity but also through differences in the structure of knowledge ecosystems that influence how innovation emerges and is evaluated over time. The study points to the value of ecosystem-level perspectives and longer-term evaluation frameworks that extend beyond short-term performance indicators.

Systems doi: 10.3390/systems14070735

Authors: Fang Li Yiman Zhao Xiao Wang

Non-attributed graph sequence offers a powerful formalism for modeling the structural dynamics of complex systems—such as social networks, urban infrastructures, and document transmission pathways—where vertex interactions evolve over time without explicit attribute information. Mining association rules from such sequences to uncover recurring topological patterns have attracted growing interest. Yet two fundamental challenges remain: (1) how to effectively encode edge-level temporal dynamics in non-attributed settings, and (2) how to perform efficient and semantically meaningful temporal association rule mining under structural uncertainty. To address these within a systems-oriented framework, we propose two novel algorithms: the weighted temporal association rule mining algorithm and the fuzzy weighted temporal association rule mining algorithm. The first algorithm introduces time-dependent numerical weights to quantify the strength and persistence of vertex connectivity, integrating them into support and confidence measures to capture both the intensity and evolution of interactions. The second algorithm extends this by incorporating fuzzy set theory, modeling ambiguous or context-sensitive relationships (e.g., indistinct links or weakly correlated vertices) and generating fuzzy-weighted rules that enhance interpretability for real-world system analysis. Evaluated through five comprehensive experiments across diverse datasets and scales using standard metrics (support, confidence, rule count, running time), our methods produce more selective rule sets and achieve lower computational times compared to the classical Apriori algorithm. The proposed approaches thus establish a robust, data-driven foundation for analyzing temporal evolution and structural uncertainty in dynamic complex systems—providing a generalizable methodology applicable beyond domain-specific constraints.

Systems doi: 10.3390/systems14070734

Authors: Tianli Huang Grace T. R. Lin

The carbon emission allowance (CEA) market is a complex socio-technical and environmental-management system in which regulatory design, trading activity, liquidity conditions, and price volatility interact dynamically. Accurate systems-level tail-risk forecasting and early warning remain challenging because carbon-market losses are affected by nonlinear dependence, episodic liquidity stress, and time-varying volatility. This study proposes an Interpretable Quantile Temporal Network (IQTN) as a systems-oriented risk-monitoring framework for China’s national CEA market. By integrating a feature-gating mechanism, a causal temporal convolutional encoder, and a non-crossing quantile output layer, IQTN directly models the conditional tail distribution of future carbon-market losses. The framework produces multi-horizon Value-at-Risk (VaR) and Conditional Value-at-Risk (CVaR) forecasts for 1-day, 5-day, and 10-day horizons and converts predicted tail risk into operational early-warning signals. Compared with historical simulation, EWMA, GARCH-type models, machine-learning quantile models, and deep temporal benchmarks, IQTN achieved the lowest 95% VaR pinball loss across all horizons, with values of 0.1765, 0.3958, and 0.5732. VaR backtesting showed empirical exceedance rates of 5.23%, 6.04%, and 6.94%, closest to the nominal 5% level. Interpretability analysis identified rolling volatility, maximum loss, intraday range, trading value, and illiquidity as key risk drivers. The temporal importance results also show that recent observations dominated the risk forecasts, suggesting that the risk state of the CEA market is highly sensitive to short-term market information. This supports the use of a short-horizon temporal network as a systems-oriented tool for carbon-market tail-risk monitoring and early warning.

Systems doi: 10.3390/systems14070733

Authors: Changshuai Li Hongyu Pan Min Zhou Zhengchu He

Against the backdrop of frequent global shocks and increasingly complex supply chain networks, supply chain disruption risk exposure has become a major challenge affecting firms’ operational stability and sustainable competitive advantage. Meanwhile, generative artificial intelligence is being increasingly embedded in business operations and has demonstrated strong application potential in information processing, risk identification, and decision support. Based on data from Chinese A-share listed firms from 2017 to 2024 and using text measures based on Management Discussion and Analysis (MD&A) disclosures of Generative AI application and supply chain disruption risk exposure, this study examines the relationship between Generative AI application and corporate supply chain disruption risk exposure, and further explores the channels through which this relationship may operate from the perspective of risk governance transformation. The results show that Generative AI application is significantly associated with lower corporate supply chain disruption risk exposure, and this relationship remains robust across a series of robustness checks and supplementary endogeneity analyses. Channel analyses suggest that this relationship may be related to firms’ risk governance transformation, mainly reflected in enhanced risk identification capability, improved resource allocation capability, and strengthened collaborative response capability. Heterogeneity analyses show that this association is more pronounced among firms facing higher environmental uncertainty, manufacturing firms, and firms located in cities with lower entrepreneurial vitality. This study provides text-based firm-level evidence for understanding the relationship between Generative AI application and supply chain risk governance, and offers managerial implications for firms seeking to promote scenario-based Generative AI application and enhance supply chain resilience and risk governance capability.

Systems doi: 10.3390/systems14070732

Authors: Yue Sun Xiaobing Liu Xiaowei Li

In online retail, consumers cannot experience product quality before purchase. With the adoption of artificial intelligence (AI), platforms can certify product quality information. However, stronger platform certification may reduce sellers’ incentives to disclose and limit personalized information such as product fit. This study examines the conditions under which a platform should adopt AI-assisted platform certification (AIPC). We develop a game-theoretic model with one platform and two competing sellers. We compare the case of not adopting AIPC with adopting AIPC, and examine how AIPC affects seller disclosure, pricing, and profits. Sellers decide whether to disclose product information and set prices. Consumers update their quality beliefs based on seller disclosure and platform labels. Our results show that AIPC is not always the preferred strategy. When product-fit information spillovers between competing sellers are strong, the platform may be better off not adopting AIPC. When information spillovers are weak, AIPC adoption depends on consumers’ prior belief regarding product quality. Specifically, when consumers have a low prior belief that an uncertified or undisclosed product is of high quality, AIPC benefits the platform and sellers but reduces consumer surplus. When this prior belief is sufficiently high, AIPC creates a win–win–win outcome for the platform, sellers, and consumers.

Systems doi: 10.3390/systems14070731

Authors: Xuexin Zhang Lin Zhang

Sustainability transitions confront firms with the following informational paradox: the regulatory pressure to innovate green has intensified, yet the knowledge required to do so is dispersed across agencies, sectors, and jurisdictions that rarely speak to one another. Treating data as a strategic factor of production, this paper asks whether and how opening public data—the systematic release of government-held datasets—reconfigures the conditions under which firms generate green innovation. We model the green-innovation ecosystem as a Complex Adaptive System (CAS) in which heterogeneous, bounded-rational agents co-evolve with a data-mediated selection environment. Within this frame, public data openness (PDO) is not marginal input but an exogenous shock to the fitness landscape that propagates through three coupling channels—supply–demand alignment, recalibration of government intervention, and amplification of green credit. Formal derivations link each channel to a testable proposition, and a multi-period Difference-in-Differences (DIDs) design built on the staggered roll-out of Chinese municipal open-data platforms identifies the causal effects, with Callaway–Sant’Anna estimators and double/debiased machine learning (DDML) addressing recent econometric critiques. The evidence supports each proposition and reveals the following distinctive heterogeneity signature consistent with absorptive-capacity heterogeneity: the policy is most consequential where agents and ecosystems are best able to convert data into knowledge. Reframing PDO as a systemic enabler clarifies why uniform rollouts yield uneven returns and motivates a tiered design that scales with the absorptive capacity of recipient firms and regions.

Systems doi: 10.3390/systems14070730

Authors: Yang Du Kui Deng Ziyang Liu

This study examines user responses to an AI travel assistant in a cross-border tourism service system. Moving beyond adoption-centered technology acceptance research, it conceptualizes these responses as a staged appraisal process in which social and experiential cues shape performance expectancy and effort expectancy, which then influence attitude and two behavioral outcomes: users’ willingness to accept AI and objections to AI. Cosmopolitanism is introduced as an individual-level boundary condition. Survey data were collected from 499 Chinese tourists holding valid South Korean tourist visas after they evaluated Visit Seoul AI, an official AI-based travel-planning tool. The hypotheses were tested using partial least squares structural equation modeling. The results show that social influence, hedonic motivation, and perceived anthropomorphism significantly affect performance expectancy and effort expectancy, which in turn shape attitude. Attitude increases usersf’ willingness to accept AI and reduces objections to AI, with a stronger effect on users’ willingness to accept AI. Cosmopolitanism strengthens the negative effect of hedonic motivation on effort expectancy. This study extends AIDUA to cross-border AI service systems and shows that users may both accept and object to AI travel assistants.

Systems doi: 10.3390/systems14070729

Authors: Andrea Gînguță Alina Elena Blehuiu Petru Ștefea Valentin Partenie Munteanu

Drawing on diffusion of innovation theory, this cross-national study examines the association between cultural dimensions and artificial intelligence (AI) capability on a 78-country sample. This cross-country, worldwide approach enables a more comprehensive understanding of differences in cross-national AI capability, providing cultural explanations for a new perspective on the diffusion of novel technologies. Our main findings reveal that individualism demonstrates the most stable positive association across model specifications. Uncertainty avoidance and motivation towards achievement and success are significant in the baseline SEM, but the results become sensitive after adding country-level control variables. Long-term orientation is significant in some OLS models but not in the baseline SEM. Power distance and indulgence are not supported in the baseline SEM. Results suggest that cultural values should be considered alongside economic, infrastructural, and regional conditions when analyzing cross-national differences in AI capability. Our findings provide a contextual perspective for policymakers and managers that are developing strategies for achieving competitive advantage. Considering the turbulence of the business and social environments, we argue that cultural adaptive capabilities are essential for global competitiveness.

Systems doi: 10.3390/systems14070727

Authors: Tao Huang Xiaoling Yuan Xinyu Yuan Rang Liu

Clarifying the sources of disparity and the systemic structure of influencing factors behind China’s economic resilience is crucial for promoting regional coordinated development and ensuring national security. This study constructs an evaluation index system based on the DPSIR model and employs the entropy method to measure China’s economic resilience from 2008 to 2023, examining its temporal evolution and spatial distribution. A bi-dimensional decomposition method of Gini coefficient is applied to examine disparities from both spatial and structural perspectives. Furthermore, the DEMATEL-ISM model is employed to reveal the systemic structure of influencing factors. The findings reveal that: (1) China’s economic resilience steadily improved during the study period, showing a spatial gradient of “Eastern > Central > Northeastern > Western,” with its geographic center shifting southeastward, reflecting strong spatial dependence. (2) Disparities in economic resilience have generally widened. Inter-regional differences are the main source of spatial disparities, while variations in response dominate the structural disparities. Initially, disparities were mainly due to differences in influence between eastern and western regions, but by the end of the period, disparities in driving forces became the key contributor. (3) Influencing factors follow a four-level, three-stage hierarchical structure. Foreign capital withdrawal risks, innovation investment, technological progress, factor supply, and the output of opening-up constitute deep-level factors influencing economic resilience. This study refines the evaluation framework of economic resilience and provides important references for understanding the disparities in China’s economic resilience and developing targeted improvement strategies.

Systems doi: 10.3390/systems14070728

Authors: Zhiwang Xu Hui Xie Chengpeng Li

As the complexity, scale, and nonlinearity of modern engineering optimization problems continue to increase, traditional optimization algorithms face significant challenges in achieving high solution accuracy, fast convergence, and robust performance. To address these issues, this paper proposes a Rural Tourism Migration-based Improved Equilibrium Optimizer (RTM-IEO), aiming to enhance the global search capability and adaptive balance between exploration and exploitation. Specifically, an adaptive lens imaging opposition-based learning strategy is introduced to effectively expand the search space and maintain population diversity. A dynamic elite-guided elimination mechanism is designed to strengthen exploitation capability and accelerate convergence by reconstructing inferior individuals using high-quality solutions. In addition, a multi-stage rural tourism migration strategy is developed to dynamically regulate the search behavior across different optimization phases, enabling a more flexible and efficient search process. The effectiveness of the proposed algorithm is comprehensively validated on the CEC2021 and CEC2022 benchmark suites, where RTM-IEO demonstrates superior performance in terms of convergence accuracy, convergence speed, and robustness compared with several representative state-of-the-art algorithms. The statistical superiority of the proposed method is further confirmed through Friedman mean ranking and Wilcoxon rank-sum tests. To further evaluate its practical applicability, RTM-IEO is applied to the sustainable economic dispatch problem of a microgrid integrating renewable energy sources, including wind power and photovoltaic generation, along with energy storage systems and controllable units. The optimization objective simultaneously considers economic cost minimization and sustainable operation requirements, such as improving renewable energy utilization and reducing dependence on fossil-fuel-based generation. Experimental results indicate that the proposed method achieves a significant reduction in daily operating cost (exceeding 52% compared with benchmark algorithms), while effectively promoting low-carbon energy utilization and enhancing overall system sustainability. Overall, the proposed RTM-IEO provides an efficient and reliable optimization framework for addressing complex global optimization problems, particularly in scenarios requiring a coordinated balance between economic performance and sustainable development.

Systems doi: 10.3390/systems14070726

Authors: Einav Argaman

This theoretical article considers a case where a chief subject-area superintendent within the Ministry of Education issued a policy, and teachers implemented it more radically than intended—extending it in a way that eliminated core elements of the original mandate. Applying ecosystem theory to the case, the article advances the conceptual theorization of ecosystem theory principles—with respect to teachers’ leadership acts—by refining key components (proximity between actors and interconnectedness, roles in ecosystems, and democratization), adding nuances that the case highlights but existing theory leaves underdeveloped. It further engages with Weberian bureaucracy, street-level bureaucracy, and Weick’s loose-coupling theory as alternative frameworks, establishing ecosystem theory’s distinctive explanatory power for the leadership appropriation dynamics the case reveals. The Discussion delineates the article’s conceptual contributions and outlines research directions that further elaborate the refinements and theoretical differentiation (ecosystem theory vis-à-vis related theories) into areas beyond the scope of this article.

Systems doi: 10.3390/systems14070725

Authors: Ricardo Luvizotto Dória Gustavo Abib Ricardo José Dória Yundi Zhang

Digital Transformation (DT) increasingly relies on project-based organizing to develop and deploy new capabilities, yet corporate innovation projects frequently stall not for lack of ideas but because of recurring governance and resource-commitment bottlenecks. This study presents a micro-longitudinal, AI-enabled, and human-reviewed analysis of 711 episodes drawn from 28 weekly project governance meetings across two corporate startup initiatives participating in the same internal incubation program, conducted between November 2024 and April 2025. Employing a six-stage analytical pipeline that combines episode-level segmentation, linguistic tension markers, and a large language model (LLM) classifier, we identify 28 decision-relevant governance tensions, which are then abductively grouped into 13 project governance dilemmas and mapped onto Teece’s dynamic capabilities framework (sensing, seizing, reconfiguring). The key finding is that 62% of dilemmas are structural in nature—reflecting persistent governance design tensions between autonomy and control, compliance and agility, and centralization and decentralization—and that 69% concentrate at the seizing stage, corresponding to resource-commitment and execution decisions. This pattern indicates a governance choke point in corporate DT projects that is structural and decisional rather than ideational. By shifting attention from lagging indicators (overruns) to governance tension leading indicators, the approach supports earlier interventions to reduce decision latency and protect project delivery performance. We further synthesize two incubation-specific meso-level governance dilemmas—stakeholder engagement and compliance vs. agility—that serve as transmission mechanisms between macro structural constraints and micro-level decision bottlenecks. The AI-enabled pipeline is proposed as a replicable early-warning system for project governance tensions in organizations pursuing digital transformation.

Systems doi: 10.3390/systems14070724

Authors: Jaime Díaz-Arancibia Ana Bustamante-Mora Jeferson Arango-López Gabriel M. Ramírez Villegas

Digital transformation among small and medium enterprises (SMEs) in developing countries is limited by a persistent gap between prevailing adoption frameworks and the sociocultural realities of target populations. Frameworks such as the Technology Acceptance Model (TAM), the Technology-Organization-Environment (TOE) framework, UTAUT, and IDT were originally developed for industrialized contexts and do not adequately account for the cultural factors influencing adoption behavior in structurally distinct environments. A systematic mapping of 256 articles revealed that only 14 consider cultural behavior as a variable, and none utilize a validated cultural measurement instrument. This study introduces the Culturally Contextualized Digital Transformation Model (CC-DTM), a four-layer theoretical architecture that integrates the TOE framework, TAM constructs, and Hofstede’s cultural dimensions, operationalized as individual-level espoused values rather than national aggregate scores. The model incorporates a novel meso-level construct, Ecosystem Density, which mediates the relationship between environmental context and organizational readiness. The CC-DTM specifies 22 constructs and 15 directional hypotheses, organized into an initial empirical agenda (H1–H12) and deferred extensions (H13–H15). Additionally, a three-configuration typology based on internal SME attributes is developed. A two-phase validation roadmap, consisting of expert-panel content assessment and configurational case illustration across ten Chilean SMEs, is proposed.

Systems doi: 10.3390/systems14060723

Authors: Sorana Vatavu Oana-Ramona Lobonț Dumitrița Gîrlă Florin Costea Daniel Brîndescu-Olariu Nicoleta-Claudia Moldovan

With intensifying measures related to investor and policy requirements, corporate governance and sectoral environmental performance became a focal point for sustainability disclosure, especially in energy-intensive industries with high environmental externalities. This study evaluates whether corporate environmental governance practices in key sectors correspond to their pollution intensity and economic output, analysing a panel dataset across EU member states, for the 2000–2021 period. The empirical methodology includes ordinary least squares (OLS), fixed- and random-effects models, and dynamic system generalised method of moments (GMM) panel estimation to account for sectoral heterogeneity. Results prove that sectoral value added is an influential factor of greenhouse gas emissions, with carbon dioxide exhibiting the highest elasticity to economic activity, followed by methane emissions, and nitrous oxide displaying cross-country variations due to structural and regulatory differences. While services and manufacturing sectors partially decouple via cleaner technologies, overall growth positively correlates with emissions, and renewable energy offers limited mitigation due to scale and integration challenges. Conclusions emphasise robust governance frameworks in high-value energy sectors to meet EU climate-neutrality goals, as stronger environmental accountability attracts capital and supports sustainable development, underscoring the needs for targeted decarbonisation, regulatory coordination, and accelerated technological innovation within persistent industry disparities.

Systems doi: 10.3390/systems14060722

Authors: Ionut Anghel Tudor Cioara

Nowadays, the literature on Generative AI (GenAI) in Information Technology (IT) project management is fragmented, focusing mainly on isolated tools, specific process groups, or practitioners’ perspectives, without offering a comprehensive synthesis. Therefore, there is a lack of systematic reviews to guide researchers in effectively and responsibly leveraging GenAI, including emerging innovations such as AI agents. This paper aims to synthesize current knowledge on GenAI in IT project management, combining a PRISMA-compliant systematic review of the peer-reviewed literature, a complementary analysis of commercial and open-source platforms, and a forward-looking research agenda featuring our vision on agentic AI architectures for IT project management. For the systematic review based on academic sources we have used the Web of Science (WoS) database in our study. Studies were eligible if published between 2021 and 2026 in English, as journal articles or conference proceedings, across major publishers (IEEE, Springer, Elsevier, MDPI, ACM, and others), and indexed under computer science, engineering, or AI categories in WoS. For industry-driven analysis, sources included vendor documentation, official product pages, and publicly accessible repository specifications, selected for relevance through manual search. The review reveals that while academic research remains largely focused on prompt-based applications of foundation models such as GPT, commercial and open-source platforms have progressed toward embedding GenAI as an operational capability within project workflows. Therefore, we consider that agentic architecture represents a promising future direction for enabling autonomous task execution, collaborative decision-making, and human–AI orchestration and integration across the project lifecycle.

Systems doi: 10.3390/systems14060721

Authors: Ihsan Ibrahim Anak Agung Putri Ratna Prima Dewi Purnamasari Naoki Fukuta

This study investigates how AI-assisted plagiarism changes dishonest academic behavior in a socially interactive learning environment under different educational conditions. To this end, this study develops a scenario-based simulation to examine how AI-assisted plagiarism influences dishonest academic behavior in socially interactive learning environments. The model represents students as autonomous agents embedded in local peer networks who adapt their weekly behavior under academic pressure, institutional intervention, and available cheating options. Two behavioral scenarios are considered: a conventional plagiarism environment, in which agents choose between honest submission and direct copying, and an AI-augmented environment, in which AI-assisted plagiarism is introduced as an additional dishonest strategy. Intervention is modeled through environmental and institutional conditions, specifically detection probability and sanction severity, rather than through direct internal reward manipulation. Q-learning is used as a simplified adaptive mechanism for repeated agent choice. Experimental results show that the possibility of producing and assessing a simulation to see the availability of AI-assisted plagiarism substantially changes the behavioral composition of misconduct by increasing total dishonest behavior and shifting a large share of it toward the AI-assisted category. In the simulation, active intervention reduces dishonest behavior overall but does not eliminate AI-assisted plagiarism as the dominant dishonest strategy in the AI-augmented environment. These observations in the simulation suggest that academic misconduct in the AI era should be understood not only as a problem of deterrence but also as a problem of behavioral adaptation under changing technological and institutional conditions. To support the realism assessment of the simulation design, the study also conducts a structured qualitative plausibility review using multiple large language models under a shared prompt. Across these reviews, the model is judged to be acceptable as a first-stage stylized baseline, while important limitations are identified in agent heterogeneity, social influence depth, and the use of Q-learning as a simplified adaptive heuristic to reproduce the behaviors of actors in there.

Systems doi: 10.3390/systems14060720

Authors: Bellam Sreenivasulu R. Subramaniam

This study investigated undergraduates’ conceptualization of systems thinking (ST). An open-ended question was administered pre- and post-course. Pre-test findings revealed limited conceptualization, with most students unable to articulate core ST attributes. Post-course responses showed reasonable improvement, with seven key attributes—interconnectedness, feedback, causality, systems boundary, mapping, emergent behaviour, and synthesis—emerging to varying extents in their responses. While nearly all students indicated interconnectedness and mapping, fewer mentioned feedback and systems boundary, indicating these as higher-order cognitive skills. A continuum was also developed to categorize students’ conceptualization from inadequate to canonical; this also indicated that only a few students demonstrated engagement with the key attributes of ST. Novel analytical approaches such as attributes prevalence tables, attributes continuum, and evolution of threshold concepts have contributed to different modes for exploring ST in the responses. Findings underscore the complexity of ST and the challenges in fostering holistic conceptualization. Overall, the study highlights a nuanced engagement with the attributes of ST from the intervention and suggests that further work is necessary to better foster these among the students.

Systems doi: 10.3390/systems14060719

Authors: Ping Yu Wangbaihui Xiong Joseph Paul Chunga

The environmental effects of green finance policies involve complex systemic interactions across multiple levels, yet existing studies often adopt fragmented analytical approaches. Drawing on the multi-level perspective (MLP), this study conceptualizes the environmental impacts of Green Finance Reform and Innovation Pilot Zones (GFRIPZs) as a process of systemic green transformation involving interactions among landscape, regime, and niche levels. Using panel data of 287 prefecture-level and above cities in China from 2012 to 2022, this study applies a staggered difference-in-differences (DID) model to evaluate the environmental impacts of GFRIPZs. The results show that GFRIPZs significantly reduce both PM2.5 concentrations and CO2 emissions. Mechanism analyses based on multiple mediation models and GSEM reveal pollutant-specific differences in underlying channels. Green technological innovation (GTI) constitutes one observable pathway for PM2.5, whereas the policy effect is more closely associated with energy structure adjustment for CO2. Heterogeneity analysis further shows that PM2.5 mitigation is stronger in colder cities, while CO2 reduction is more pronounced in developed cities. These findings reveal pollutant-specific mechanisms of green finance and offer policy implications for developing countries seeking to promote systemic green transformation.

Systems doi: 10.3390/systems14060718

Authors: Jinming Mo Zhengwei Ma

Whether information consumption guides firms back to their core businesses or instead exacerbates corporate financialization remains empirically underexplored. We use panel data of Chinese A-share listed firms from 2009 to 2024. We take China’s Information Consumption Pilot policy as a quasi-natural experiment and employ a staggered difference-in-differences approach to examine the impact of information consumption on corporate financialization. The findings show that information consumption significantly promotes corporate financialization, with the precautionary motive driving financialization more strongly than the profit-seeking motive. Mechanism tests reveal that information consumption drives corporate financialization by easing financing constraints and improving investment efficiency, while internal corporate governance and external economic policy uncertainty play significant moderating roles. Heterogeneity analysis indicates that the exacerbating effect of information consumption on corporate financialization is more pronounced in non-state-owned enterprises, small-scale firms, non-high-tech industries, and regions with a low level of financial development. Further analysis shows that information consumption not only exacerbates excessive corporate financialization but also triggers peer effects in financialization. Moreover, the financialization induced by information consumption suppresses long-term corporate performance growth. These findings uncover the micro-mechanisms through which information consumption reshapes corporate capital allocation decisions, offering practical implications for refining information consumption policies and channeling financial resources back to the real economy.

Systems doi: 10.3390/systems14060717

Authors: Yanfei Li Cheng Li

Clarifying the mutual feedback relationship and coordinated evolution characteristics between low-carbon transportation (LCT) and high-quality economic development (HQED) is of great significance for the green transformation of resource-based and ecologically fragile urban agglomerations. Taking 18 cities in the Yellow River Jiziwan Metropolitan Area as the research objects, this paper constructs an evaluation indicator system for LCT and HQED based on panel data from 2013 to 2022, and comprehensively applies the ISM-MICMAC model, a modified coupling coordination degree model, a gravity model, an obstacle degree model, and a combined GM-ARIMA forecasting model to analyze the interaction relationships, spatiotemporal evolution, spatial correlations, and scenario differences between the two systems. The results indicate that: (1) A hierarchical mutual feedback relationship exists between LCT and HQED, in which the relevant factors exhibit a hierarchical association within the system structure, extending from basic input, transportation supply, and economic operation to green and low-carbon outcomes. (2) During the study period, the comprehensive development levels of the two systems generally improved, with the mean coupling coordination degree rising from 0.4374 in 2013 to 0.4702 in 2022, remaining overall at a borderline coordination stage, while inter-city divergence was relatively pronounced. (3) The spatial connection network gradually exhibited multi-node linkage characteristics, yet strong connections remained concentrated in a few core cities. (4) Scenario predictions reveal that the synergistic development scenario is most conducive to enhancing the coupling coordination level, and the differences among scenarios gradually widen after 2026. Simultaneously advancing LCT and HQED is an important pathway to enhance the regional synergy level of the Yellow River Jiziwan Metropolitan Area.

Systems doi: 10.3390/systems14060716

Authors: Mehmet Avcı Aytaç Altan Sedat Polat Yusuf Bahri Özçelik Mehmet Pekkaya Gökhan Dökmen

Water and sanitation governance sits at the intersection of global development ambitions and highly localized service realities. While SDG6 sets universal targets for clean water and sanitation, the institutional and fiscal arrangements that translate those targets into actual service outcomes operate primarily at the subnational level. The discrepancy between globally defined objectives and locally executed delivery creates a structural research gap: how do the fiscal architectures of local governments influence progress towards SDG6? This study addresses this question for a panel of OECD countries by developing a deep learning-based estimation framework that combines bidirectional long short-term memory (BiLSTM) networks with Tianji’s horse racing optimization (THRO) algorithm. Three distinct operationalizations of fiscal decentralization are tested against SDG6 outcomes: subnational expenditure share (EFDM), subnational revenue share (RFDM), and a composite index balancing both dimensions (CFDM). Model adequacy is assessed using a layered diagnostic protocol involving regression fit, country-level residual patterns, error density profiles, Bland–Altman limits of agreement and inter-annual error trajectories. Among the three configurations, CFDM consistently records superior performance (R2=0.9216; RMSE = 1.4465; MAE = 1.0712), while even the weakest specification clears R2=0.89, attesting to the overall robustness of the proposed architecture. The margin by which CFDM outperforms its alternatives highlights a key finding: neither spending authority nor revenue capacity alone accurately reflects the fiscal reality of local water and sanitation governance; it is their combined effect that is important. The expenditure dimension is further proven to be the more influential of the two unidimensional proxies, consistent with the capital-intensive and maintenance-heavy nature of water infrastructure. On the other hand, coefficient findings show that fiscal decentralization is positively associated with SDG6 achievement for all models. Beyond its empirical contributions, the study introduces a methodological template for applying hybrid AI optimization to policy-relevant sustainability panels. It also connects two largely parallel bodies of scholarship, fiscal federalism and SDG research, that have rarely been examined together.

Systems doi: 10.3390/systems14060715

Authors: Yanling Zheng Shizhen Jiang Haiquan Chen Guojie Xie Yu Tian

During the stage of high-quality economic development, the synergy between advancing county industrial structure and employment growth has become a key issue in county governance. Although existing studies confirm that industrial structure has both creation and substitution effects on employment, few have adopted a configurational perspective to reveal how combinations of multiple factors can jointly promote both advanced county industrial structure and employment growth, thereby achieving industry-employment synergy. From the perspective of inclusive growth, this study incorporates six factors-economic level, financial level, innovation level, human capital, fiscal expenditure, and agricultural resources-into a unified analytical framework under the dimensions of efficiency and equity. Using a mixed method that combines dynamic QCA and regression analysis, and taking 1128 Chinese counties as the sample, this study explores configurational pathways that can simultaneously achieve advanced county industrial structure and inclusive employment growth. The findings are as follows: (1) Four configurational pathways lead to advanced county industrial structure: market-driven with efficiency priority (C1), endowment-substituted with factor concentration (C2), endowment-dependent with efficiency-equity coordination (C3), and talent–innovation dual-driven with government assistance (C4). (2) These four pathways differ in their effectiveness in promoting industry–employment synergy. Configurations C1, C2, and C3 achieve coordinated development of county industry and employment, whereas configuration C4 promotes advanced county industrial structure but inhibits employment growth. The conclusions reveal multiple equivalent pathways for synergistically enhancing county industry and employment, providing a basis for local governments to formulate context-specific industry–employment coordination policies.

Systems doi: 10.3390/systems14060714

Authors: Debao Dai Huiying Li Min Zhao

In the context of an increasingly complex and dynamic digital ecosystem, accurately assessing the value of Specialized, Refined, Differentiated, and Innovative (SRDI) enterprises is crucial for making effective decisions. Traditional valuation methods struggle to effectively address issues such as the high R&D expenditures and significant operational risks associated with these enterprises. This study proposes an interpretable intelligent decision-support framework for valuing SRDI enterprises listed on the Beijing Stock Exchange (BSE), constructing a multidimensional indicator system that encompasses solvency, profitability, and R&D capabilities. Feature importance screening using the XGBoost algorithm was conducted to identify key indicators as input variables for a backpropagation (BP) neural network. Concurrently, the Particle Swarm Optimization (PSO) algorithm was applied to the neural network to optimize initial weights and thresholds, thereby modeling nonlinear valuation relationships. Empirical analysis of 770 SRDI firms listed on the Beijing Stock Exchange from 2020 to 2024 indicates that the XGBoost-PSO-BPNN model achieved a coefficient of determination of 0.8083 on the test set, outperforming traditional linear models and benchmark models such as single-tree models. SHAP explainability analysis further reveals that current asset turnover, return on assets, and equity concentration are the primary value drivers. This study employs various clustering methods to further classify enterprises into three categories and proposes recommendations for differentiated regulatory policies, providing intelligent decision support for enterprises operating within complex digital ecosystems.

Systems doi: 10.3390/systems14060713

Authors: Beril Gül Ayberk Soyer

The existing literature highlights that artificial intelligence (AI) creates both hope and threat perceptions among managers and workers, particularly due to concerns about potential job losses and the negative effect on continued professional development. Employee trust in AI-based systems varies depending on their features and performance. Furthermore, regardless of the performance of such systems, some individuals are inherently opposed to AI, a phenomenon known as AI aversion. In this study, an Integrative AI Adoption Framework is developed, drawing upon principles from established theories, including the technology acceptance model, behavioral decision theory, and emotion-based frameworks, to assess how perceived usefulness and perceived ease of use, along with perceived threat, trust, and AI aversion, influence human resources (HR) professionals’ attitudes and behavioral intentions to use AI-based recruitment systems. In doing so, the study conceptualizes AI-based recruitment as a socio-technical system in which a technical subsystem (the system’s instrumental and AI-specific properties) and a social subsystem (the affective and trust-related responses of HR professionals) must be jointly considered to explain adoption. The model was tested using the partial least squares structural equation modeling (PLS-SEM) approach through survey-based data collected from 242 HR professionals. The study’s findings indicate that attitude plays an important role in shaping behavioral intention, and perceived usefulness is a key driver of attitude. AI aversion negatively influences attitudes, while trust has a twofold effect of reducing AI aversion and positively influencing attitude. Additionally, perceived threat significantly increases AI aversion, which is driven by concerns over job replacement and personal development.

Systems doi: 10.3390/systems14060712

Authors: Valeria Samajova Lucia Duricova

The expansion of remote work has transformed labour market conditions across the developed world, yet access to home-based work remains unequally distributed along occupational, sectoral, regional, and organisational lines. Post-pandemic evidence on the persistence of these inequalities is particularly scarce in Central and Eastern European economies, where historically low remote work prevalence, manufacturing-intensive industrial structures, and pronounced regional disparities create a distinctive structural context. Drawing on primary survey data collected from 390 employees in Slovakia in 2025, this study pursues two interrelated empirical goals: to identify the factors predicting a mismatch between the structural feasibility of working from home and its actual availability to employees, and to examine the determinants of experienced work discomfort. Binary logistic regression, multiple linear regression, and a battery of group difference tests were employed across the two analytical strands. The results reveal a pronounced capital–periphery gradient in remote work access, with employees outside the capital city facing dramatically higher odds of mismatch, and identify organisational support as the most practically actionable determinant of work discomfort. Notably, experiencing a mismatch between remote work feasibility and access was not associated with higher discomfort, a finding that challenges assumptions common in the Western European literature and points to the moderating role of contextual expectations in post-communist labour markets. The findings offer directly applicable evidence for employers seeking to reduce work-related strain through targeted support measures, and for policymakers designing regulatory frameworks to promote equitable access to flexible work arrangements across regions and sectors.

Systems doi: 10.3390/systems14060711

Authors: Chaokai Xue Yulong Chen

The escalating global climate crisis has increased scholarly and practical attention to green investment as a key driver of corporate sustainability. From a systems perspective, enterprises can be viewed as complex socio-technical systems in which green resource allocation, technological innovation, and digital transformation interact dynamically. Against this background, this study examines how green investment (GI) affects corporate digital value (DV) and whether green technological innovation (GTI) serves as a transmission mechanism in this relationship. Using panel data from 15,244 firm-year observations of Chinese A-share listed companies from 2012 to 2024, this study applies panel data estimation methods to test the proposed relationships. The results show that GI significantly enhances DV, indicating that green resource allocation can strengthen firms’ digital value creation. GTI plays a partial mediating role in the relationship between GI and DV, suggesting that green investment contributes to digital value not only directly but also by stimulating technological innovation within the corporate system. Further heterogeneity analysis reveals that the positive effect of GI on DV is more pronounced among state-owned enterprises and firms located in eastern regions. These findings enrich the literature on green–digital transformation by highlighting the systemic linkage between green investment, green technological innovation, and digital value creation. They also provide practical implications for policymakers and corporate managers seeking to promote coordinated low-carbon and digital development through more effective green investment and innovation strategies.

Systems doi: 10.3390/systems14060710

Authors: Chunxu Shen Shuyang Yao

Identifying emerging research frontiers is essential for tracking disciplinary developments and institutional strategic planning. However, existing methods for topic identification present several limitations, including insufficient semantic understanding, difficulty in reducing redundancy, and instability in generating and clustering topics without manual intervention. To address these challenges, we propose a systematic framework that integrates large language models (LLMs), a semantic embedding model, and quantitative indicator evaluation. Applying this framework to engineering management, we construct a delimited corpus of 350 synthesis-oriented articles from the Web of Science (WoS) and obtain standard topics ranked by a composite score incorporating frequency, centrality, and novelty scores. Then we carry out five duplicate experiments and successfully cluster eight major research directions from all the standard topics. The results are robustly tested, providing a solid evidence base for scientific management and data-driven policy making in this field. The proposed research framework not only supports engineering management research, but also offers a promising approach for identifying emerging research frontiers in other disciplines.

Systems doi: 10.3390/systems14060709

Authors: Lara J M Naser Alper Göksu Berrin Denizhan

Supplier selection in healthcare is a complex multi-criteria decision-making (MCDM) challenge requiring a balance of sustainability, resilience, and operational efficiency. Traditional methods struggle with scalability and subjectivity when applied to large administrative datasets. This study introduces a transparent hybrid Machine Learning–MCDM (ML–MCDM) framework, validated using a U.S. Medicare dataset of 661 suppliers. The framework integrates eXtreme Gradient Boosting (XGBoost) and SHapley Additive exPlanations (SHAP) for criterion prioritization, the Full Consistency Method (FUCOM) for mathematically consistent weighting, and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) for final ranking. As the dataset lacks direct sustainability metrics, seven indicators were synthetically generated; thus, the results serve as proof-of-concept demonstration of the framework’s architecture. Specifically, XGBoost–SHAP is trained to predict a synthetically constructed Overall Performance Score (OPS), meaning that the resulting feature importance output constitutes an algorithmic consistency check—confirming that the pipeline correctly recovers importance signals deliberately embedded in the training target. For interpretability, suppliers were segmented into five performance profiles via K-Means: Strategic Partners (17.7%), Green Leaders (18.6%), Reliable Emergency Suppliers (18.2%), Balanced Performers (20.4%), and Developing Suppliers (25.1%). Carbon Footprint Score (0.408) and Emergency Response Capability (0.316) achieved the highest feature importance. FUCOM-derived weights prioritized On-Time Delivery Rate (0.272), Carbon Footprint Score (0.222), and Emergency Response Capability (0.220). The top supplier attained a TOPSIS closeness coefficient of 0.800, showing strong discrimination. Sensitivity analysis across four scenarios confirmed ranking robustness, maintaining Spearman correlations ρ ≥ 0.977. This ML–FUCOM–TOPSIS approach provides an auditable, scalable, and policy-relevant decision-support tool, enabling procurement managers to navigate high-dimensional data while ensuring operational continuity and environmental responsibility in healthcare supply chains.

Systems doi: 10.3390/systems14060708

Authors: Shuo Zhao Feng Zhang

The rapid advancement of large language models (LLMs) and generative artificial intelligence (Gen-AI) has profoundly reshaped the landscape of education [...]

Systems doi: 10.3390/systems14060705

Authors: Changjiang Zhang Sihan Zhang Zhepeng Zhou Kongwen Wang

Fulfilling corporate ESG responsibilities enhances a firm’s sustainable development capabilities but also comes at an economic cost. This study investigates whether firms should invest heavily in ESG or maintain moderate ESG practices to balance cost efficiency and resilience. Using a sample of A-share listed companies in China from 2012 to 2024, we employ OLS regression models to explore the impact of ESG responsibility fulfillment on cost stickiness and the factors that influence this relationship. The study finds that (1) there is an inverted U-shaped relationship between corporate ESG responsibility fulfillment and cost stickiness; (2) the turning point lies between the B and CCC Huazheng ESG rating levels. Below this level, ESG responsibility fulfillment reduces cost stickiness, while above it, excessive ESG fulfillment increases cost stickiness; (3) environmental sensitivity, managerial overconfidence, and state ownership amplify this non-linear effect, making the reduction or increase in cost stickiness more pronounced. This paper deepens the understanding of the drivers of cost stickiness from the perspective of ESG responsibility fulfillment, offering new insights for future research on cost behavior and providing valuable guidance for firms seeking to optimize cost management through ESG strategies.

Systems doi: 10.3390/systems14060707

Authors: Panpan Yang Haihong Yuan Yaxin Ge Wenxuan Cao Yanke Li Renfeng Ma

Small-scale island communities whose livelihoods depend on aquaculture are increasingly vulnerable under interacting climatic and non-climatic stressors. Conventional indicator-based assessments are useful for describing the level of vulnerability, but many empirical assessments remain less able to explain how multiple stressors are mediated through local social–ecological structures and feedback processes to produce different vulnerability patterns. This study aims to explain how vulnerability is formed in island aquaculture communities by linking social–ecological system structures with vulnerability processes and by examining empirically informed feedback pathways. Drawing on evidence from three island aquaculture communities in southeastern China, household survey data were first used to classify community types through hierarchical clustering. Semi-structured interviews, field observations, and documentary materials were then qualitatively coded to develop empirically informed conceptual causal loop diagrams (CLDs) for each type. Key variables and recurring feedback pathways were identified through loop-based structural analysis and cross-case comparison. The analysis indicates that vulnerability formation in island aquaculture communities is associated with recurring reinforcing feedbacks within local social–ecological system structures, through which multiple climatic, ecological and socio-economic stressors are translated into differentiated vulnerability outcomes. Across the case communities, resource overexploitation and marine pollution reinforce an ecology–livelihood degradation loop, while labor outmigration erodes social capital, disrupts intergenerational knowledge transmission, and weakens collective action and adaptive capacity, exacerbating socio-ecological vulnerability. At the same time, dominant stressors, key drivers, and feedback configurations vary across community types, generating divergent vulnerability trajectories and highlighting the context-dependent nature of vulnerability dynamics. These results suggest that governance interventions targeting isolated stressors or relying on static vulnerability analyses are insufficient where reinforcing feedbacks dominate. Effective adaptation strategies should explicitly target critical feedback pathways and strengthen stabilizing processes. By integrating social–ecological systems thinking with vulnerability analysis, this study provides a feedback-oriented approach for diagnosing vulnerability formation and supports more feedback and context-sensitive governance in small-scale island aquaculture communities.

Systems doi: 10.3390/systems14060706

Authors: Jieying Gao Duyang Zhou Shengjie Zhou

In the digital era, intelligent applications play an increasingly pivotal role in restructuring supply chain cost management. Using panel data from Chinese-listed firms between 2010 and 2024, this study examines the impact of customers’ Artificial Intelligence (AI) adoption on the cost stickiness of their suppliers. The findings indicate that customers’ AI adoption mitigates suppliers’ cost stickiness. This effect is more pronounced for larger suppliers, those with shorter geographic distance to customers, and those in highly competitive industries. Furthermore, customers’ AI adoption alleviates suppliers’ cost stickiness by promoting flexible production modes, enhancing production information flexibility, and raising production efficiency. Moreover, a two-stage model suggests that this alleviation of cost stickiness enhances suppliers’ corporate resilience, offering directional insights for transmitting within supply chain systems. In summary, this paper expands the theoretical understanding of intelligent applications in supply chain systems, by substantiating cross-firm spillover effects and interactive behaviors among supply chain stakeholders.

Systems doi: 10.3390/systems14060704

Authors: Xin Zhang Jie Peng Wanhua Liu Yutong Hao Xingwei Li

Numerous construction enterprises have insufficient efficiency in resource utilization for construction and demolition waste (CDW), restricting global circular economic development. How to improve resource utilization has become an urgent problem. While existing studies have extensively explored operational decisions in CDW resource supply chains, insufficient attention has been given to construction enterprises’ information misreporting and its interaction with on-site conversion efficiency. This paper aims to elucidate the mechanism of action of misreporting and systematically analyzes its effects on the pricing decisions of the CDW supply chain. Drawing on information misreporting theory, this study constructs a Stackelberg game model involving construction firms and recycled building materials manufacturers, and compares supply chain decision-making behaviors under two scenarios: information misreporting and honest disclosure. The main conclusions are as follows: (1) misreporting alters recycled building material pricing and profit distribution by affecting manufacturers’ supply capacity expectations; (2) higher on-site conversion efficiency enhances CDW treatment ability and affects stakeholders’ profits; and (3) misreporting is related to on-site conversion efficiency and onsite conversion costs—enterprises prefer misreporting for short-term gains under low on-site conversion efficiency or high costs, while higher on-site conversion efficiency makes truthful disclosure conducive to long-term stable returns. This paper reveals the CDW supply chain decision-making mechanism from enterprises’ perspective, providing a new theoretical basis and practical value for CDW utilization and supply chain optimization.

Systems doi: 10.3390/systems14060703

Authors: Ken Spours Liying Rong

This article provides the theoretical foundation for upcoming primary research on the formation of ‘integral generative AI (GenAI) innovation ecosystems’ in the Chinese higher education context. Based on an adaptation of Gramsci’s idea of the ‘integral state’, which informs the move beyond Western civil society/market-led and Chinese political state-led innovation ecosystem models, key features of an integral innovation GenAI ecosystem are elaborated upon. An expanded framework builds on previously published work on socialised GenAI systems comprising a multi-level approach, with particular emphasis on ‘thickened’ meso-institutional layers (e.g., supportive local investment, institutional governance frameworks and critical practices) mediating between an enhanced macro-strategic direction and upscaled micro-level practices. Theorising the institutional meso-system helps analyse challenges facing non-elite Chinese universities in moving from a ‘low-technological-baseline equilibrium’ (LTBE) constraining GenAI development to demonstrating features of GenAI innovation ecosystem ‘readiness’. The framework also draws on Lury’s ‘problem space’ research methodology, with a particular focus on its ‘within/without’ contextual factors, while also contributing a chrono-dimension to reinforce its conceptual role over time. The article concludes with an outline of a primary research strategy to investigate the challenges of building integral GenAI innovation ecosystems in Chinese higher education institutions more broadly.

Systems doi: 10.3390/systems14060702

Authors: Ningning Huang Jinzhuo Wu

The past few years has witnessed the rapid development of China’s logistics industry. However, the industry still faces problems such as uneven regional development, low-cost efficiency, insufficient technology application, and pressure for green transformation. To support more effective policy and strategic planning, this study used composite location entropy, spatial autocorrelation analysis, and kernel density estimation to analyze the spatiotemporal evolution of logistics industry agglomeration based on China’s city-level panel data from 2010 to 2023. Geographic detectors and geographically weighted regression were used to explore its driving mechanisms from multiple perspectives. The results indicated that (1) China’s logistics industry agglomeration exhibited a decreasing gradient from east to west and the regional disparities gradually narrowed down over time. (2) China’s logistics industry showed significantly positive spatial autocorrelation, characterized mainly by high-high and low-low clusters. Northeastern China experienced the most active and tortuous local spatial evolution of logistics agglomeration, while Eastern China exhibited high tortuosity but stable spatial structure. Western China showed a smooth evolution, and Central China followed a relatively independent evolutionary path. Spatially, China’s logistics industry presented a pattern of high concentration in the southeast and sparse distribution in the northwest, with high-value zones expanding toward the central and western regions. (3) Transportation accessibility was the primary factor influencing logistics industry agglomeration, and the interaction among factors was stronger than the effect of individual factors. Specifically, the degree of openness exhibited a driving pattern centered on coastal areas and decreasing towards inland regions; the level of commercial development showed a positive correlation in the west and a negative correlation in the east; the spatial pattern of transportation capacity shifted from a pronounced east–west polarization to a more fragmented multi-cluster distribution; and transportation accessibility demonstrated spatial heterogeneity, with positive correlation in the southeast coastal areas and negative correlation in the west.

Systems doi: 10.3390/systems14060701

Authors: Jun Dong Guo Zeng Jie Xue

Against the backdrop of widening regional disparities and the rapid expansion of digital connectivity, understanding the relationship between new quality productive forces (NQPF) and urban–rural integration requires a systemic and network-based perspective. This study approaches urban–rural integration from a complex adaptive system perspective embedded in dynamic interregional networks. Using panel data from 31 Chinese provinces from 2014 to 2024, we construct composite indices for NQPF and urban–rural integration and combine two-way fixed-effects models, static Spatial Durbin Models (SDM), and dynamic-network two-way fixed-effects spatial-lag specifications. This framework helps examine local associations, network-based spillover patterns, and heterogeneous system responses. The results show that: (1) urban–rural integration exhibits significant spatial clustering, with Moran’s I becoming positive and statistically significant after 2016, reflecting persistent structural imbalances within the regional system; (2) the static SDM results show that NQPF is positively associated with urban–rural integration both locally and through spatial indirect linkages; (3) compared with conventional static geographic matrices, the dynamic network-based spatial weights provide additional information on evolving interregional linkages shaped by economic proximity, digital capability similarity, and factor mobility; and (4) under the dynamic network-based specification, NQPF remains positively associated with network exposure in connected provinces, with heterogeneous patterns across regions. More stable local associations are observed in high-connectivity and eastern regions, while the low-connectivity group and central–western regions appear to benefit more from network-based linkages. These findings suggest that the relationship between NQPF and urban–rural integration is embedded in a spatially connected and network-conditioned regional system. By integrating spatial econometrics with a complex systems perspective, this study provides a complementary framework for understanding regional transformation in the digital era.

Systems doi: 10.3390/systems14060700

Authors: Shu Fang Heliang Zhu Huilu Jiang Zouxian Yan

The transition toward sustainable manufacturing requires an understanding of how industrial policies shape firms’ long-term green innovation capabilities. This study investigates the impact of China’s intelligent manufacturing pilot policy on enterprises’ sustainable green innovation, conceptualizing the policy as an exogenous driver of systemic transformation at the firm level. Using multi-period difference-in-differences (DID) regression on an unbalanced panel dataset of Chinese listed companies from 2010 to 2023, we find that the intelligent manufacturing pilot policy exerts a significantly positive effect on enterprises’ sustainable green innovation. Mechanism analyses reveal that the policy promotes sustainable green innovation through three pathways: facilitating digital transformation, alleviating financing constraints, and enhancing ESG performance. Heterogeneity analysis further indicates that the policy effects are more pronounced in eastern regions, among non-state-owned enterprises, in non-heavily polluting industries, and in technology-intensive industries. These findings provide insights into how systemic policy interventions can drive sustainable innovation at the firm level, with implications for policymakers and enterprises seeking to align industrial upgrading with long-term green development. These findings are interpreted through a system transformation lens, where intelligent manufacturing policies trigger co-evolutionary changes across digital, financial, and governance subsystems.

Systems doi: 10.3390/systems14060699

Authors: Yuefei Zhuo Hangang Hu Guan Li

Urban vitality is regarded as a cornerstone of sustainable urban development. While land use mix (LUM) is widely acknowledged for fostering vitality, most empirical evidence relies on mean-effect models, neglecting the heterogeneous impacts across different vitality levels. This overlooks the complex, context-dependent nature of LUM and risks perpetuating one-size-fits-all planning. Based on a theoretical framework that links LUM analysis with contemporary urban revitalization, public governance, and smart city development discussions, this study leverages a Spatial Durbin Quantile Regression (SDQR) framework with multi-source geospatial data from 511 blocks in Ningbo, China, to systematically investigate the distributional heterogeneity of LUM’s effects on urban vitality. We decompose LUM into “diversity”, “proximity”, and “coordination” dimensions, revealing three distinct mechanisms across the vitality spectrum. Results show “coordination” acts as a fundamental “ignite” mechanism, consistently driving vitality across all quantiles, especially in new towns and low-vitality areas. “Diversity” primarily serves as a “boost” mechanism, enhancing vitality in medium-to-high vitality areas, demonstrating a non-linear, conditional effect. Crucially, “proximity” exhibits a novel “cap & siphon” mechanism: its direct effect is often insignificant or negative in low-vitality areas (suggesting structural mismatch), while its significant negative spatial spillover effect (siphon effect) across all quantiles, particularly in low-vitality zones, highlights intense inter-area competition. Furthermore, LUM’s direct effects tend to diminish in high-vitality areas, indicating a saturation or “cap” effect. By revealing these heterogeneous impacts and spatial spillover dynamics, this research refines the boundary conditions of classic mixed-use propositions and provides a differentiated planning paradigm, moving from universal zoning to context-specific, stage-calibrated interventions that address areas based on their current vitality levels, spatial interactions and governance contexts.

Systems doi: 10.3390/systems14060698

Authors: David Alkaher Elizabeth J. Taylor Michal Frenkel Yacov Bengo

Hierarchical Public Sector Organizations (PSOs), particularly military organizations, face persistent challenges in sustaining innovation due to structural rigidity, hierarchical control, and embedded resistance to change. While existing literature explains why innovation emerges and why it is resisted, significantly less attention has been devoted to understanding how innovation becomes institutionalized as a sustained organizational capability. This study addresses this gap by introducing the Bi-focal Innovation Contagion Model (BICM), an agent-based framework that conceptualizes innovation diffusion and resistance as a co-evolutionary “tug-of-war” between competing organizational forces. The model integrates top-down governance mechanisms and bottom-up innovation processes, capturing how heterogeneous actors interact within hierarchical systems to shape the diffusion, assimilation, and stabilization of innovation over time. Using the Israel Defense Forces (IDF) as an empirical source case, the model explores how Innovation Management Systems (IMS) may be designed to support the institutionalization of innovation as a self-sustaining organizational capability within hierarchical PSOs. Simulation results suggest that hybrid innovation architectures may better sustain innovation across varying leadership conditions. This occurs when centralized strategic coordination is combined with decentralized innovation activity and supported by mature innovation agents with sufficient centrality and hierarchical reinforcement. The findings highlight the critical role of IMS as an organizational architecture for achieving anti-fragility, enabling innovation dynamics to persist, adapt, and strengthen in the face of uncertainty, leadership turnover, and shifting strategic priorities. By integrating agent-based modeling with organizational theory, this study contributes a dynamic framework for understanding and designing sustainable innovation systems in hierarchical PSOs.

Systems doi: 10.3390/systems14060697

Authors: Katharina Polanec Simon Eschlberger Markus Peter David Hoffmann Arndt Lüder

Rising complexity in cyber-physical systems development exposes challenges in the consistent and reusable specification of graphical domain-specific languages (DSLs). Despite the benefits of model-based systems engineering (MBSE), the absence of a standardized, life-cycle-wide specification process results in semantic inconsistencies, tool dependence, and limited interoperability. While our previous work has addressed individual stages of DSL definition, a comprehensive, standards-based process integrating these stages remains missing. Building on these foundations, this paper introduces a unified language specification process for graphical DSLs grounded in established standards—the Meta-Object Facility (MOF), Unified Modeling Language (UML), Web Ontology Language (OWL), and Resource Description Framework (RDF). The process integrates three core artifacts: a tool-independent ontology capturing domain semantics, a MOF-conforming metamodel unifying abstract syntax, semantics, and concrete syntax, and a UML-profile-based implementation. To support and exemplify this process, a prototypical toolchain is introduced that enables automated transformations between these artifacts, thereby facilitating the consistent propagation of semantics from ontology to implementation. The applicability of the proposed process is demonstrated through both a top-down automotive case and a bottom-up cybersecurity DSL, illustrating its cross-domain generalizability. By explicitly structuring and connecting ontology, metamodel, and implementation, this work contributes a semantically consistent, machine-interpretable, and tool-independent specification process for graphical DSLs in MBSE.

Systems doi: 10.3390/systems14060696

Authors: Cihat Ozturk Abdullah Yildizbasi

The Digital Product Passport (DPP) is considered a critical tool for sustainable supply chains within the scope of the European Green Deal. DPP significantly contributes to improving traceability, transparency, reliability, and circularity in supply chains, enabling a more robust and secure structure. However, despite this significant potential, achieving full integration of DPP is hampered by various organizational, technological, and environmental barriers. This study used the Grey Decision Making Testing and Evaluation Laboratory (Grey DEMATEL) approach, the Technology–Organization–Environment (TOE) framework, and Force Field Theory to identify and categorize these barriers. A total of 27 barriers were identified based on a comprehensive literature review and the opinions of academic and industry experts, and these barriers were categorized into organizational, technological, and environmental categories. The study findings demonstrate that technological barriers, in particular, have a causal effect that strongly triggers both organizational and environmental challenges. The causal analysis conducted reveals the interdependencies among barriers and guides practitioners and policymakers in identifying resistance points to change. Furthermore, the study offers important insights that will help supply chain stakeholders transition from reactive approaches to proactive strategies when managing DPP-related barriers. The insights gained in this regard support the design of collaborative governance mechanisms to create a more resilient, transparent, manageable, secure, and circular supply chain ecosystem.

Systems doi: 10.3390/systems14060695

Authors: Alberto Blázquez-Pérez Pedro Fernández Sánchez

The aim of this study is to analyse the factors associated with sustainable entrepreneurship in Spain from a systemic perspective, highlighting the interaction between economic, cognitive, occupational and axiological factors that shape innovation and sustainability in digital environments. Using microdata from the Global Entrepreneurship Monitor Spain 2021, a Probit model is estimated to identify which variables are associated with TEA environmental consideration (TEA-EC), defined as the probability that early-stage entrepreneurs report considering environmental implications when making decisions about the future of their business. The results show that age, certain occupations (particularly part-time work, unemployment and self-employment), self-perceived entrepreneurial skills and values associated with social impact are the main factors associated with environmentally oriented entrepreneurship. Conversely, education, income, innovation, internationalisation and technological intensity are not significant, while gender is statistically associated with TEA environmental consideration (TEA-EC) in a context-dependent manner, particularly through its interactions with sectoral affiliation and social-impact orientation. Significant sectoral differences are also observed. The variables most strongly associated with TEA-EC are concern with social impact and the prioritisation of socio-environmental outcomes over profitability, each of which is associated with a higher likelihood of environmentally oriented decision-making among early-stage entrepreneurs by more than 23 percentage points. The study concludes that sustainable entrepreneurship in Spain is primarily associated with internal capabilities and pro-environmental values, rather than with structural incentives, offering key implications for the design of policies aimed at sustainable entrepreneurial systems.

Systems doi: 10.3390/systems14060694

Authors: Yue He Xia Fan

The deep integration of sci-tech and industrial innovation, rooted in the fusion of exploratory and exploitative ambidextrous capabilities, is a common global challenge. Traditional actors like enterprises and universities struggle due to the inherent imbalance of ambidextrous capability. Developed countries (e.g., Germany’s Fraunhofer, Finland’s VTT) have achieved integration through new-type research organizations, but rely on a “static coordination” model across departments ill-suited for rapidly changing, multi-logic environments. In contrast, China’s new-type R&D institutions (NTRI), emerging as innovative organizations, are naturally equipped to handle such institutional complexity and have become key drivers of deep integration. This study takes NTRI as a longitudinal single-case study object. Based on ambidextrous innovation theory and resource action theory, it constructs an analytical framework of “identifying integration challenges—addressing integration challenges—achieving integrated innovation” to explore how NTRI build dynamic ambidextrous capability through resource actions to drive the internal mechanism of integrating sci-tech innovation and industrial innovation. The results show that: (1) Accurately identifying integration breakpoints, bottlenecks, and hurdles at different development phases and establishing integration goals are key prerequisites for achieving integrated innovation; (2) the process of achieving integrated innovation is essentially a dynamic reconstruction of ambidextrous capability, involving resource bricolage to reconfigure demand-driven ambidextrous linking capability, utilizing resource orchestration to fission context-synchronized ambidextrous integration capability, and executing resource concerto to leapfrog networked symbiotic ambidextrous empowerment capability; and (3) the integrated innovation of NTRI at different phases exhibits a dynamic evolution, evolving from unidirectional spillover-integrated innovation to bidirectional interactive integrated innovation, and ultimately to empowering symbiotic integrated innovation.

Systems doi: 10.3390/systems14060693

Authors: Kuk-Kyoung Moon Jaeyoung Lim

Racial diversity is normatively desirable in public organizations, but the social and psychological processes it activates may lead to employees’ negative work attitudes. Combining social categorization theory, perceived organizational support theory, and psychological contract theory, this study investigates whether racial diversity is negatively related to employee work engagement in U.S. federal agencies and whether the perceived effectiveness of agency ethics programs moderates this relationship. Using multilevel mixed-effects regression analyses with data from 10,088 employees nested within 24 federal agencies drawn from the 2016 Merit Principles Survey, we find that racial diversity was negatively associated with work engagement. However, this negative relationship was reduced when employees perceived their agency’s ethics program as more effective. At high levels of perceived effectiveness, the negative association was no longer statistically significant. These findings suggest that the perceived effectiveness of ethics programs is a meaningful organizational condition under which the negative association between racial diversity and work engagement may be attenuated. This pattern has implications for diversity management and human resource practice in ethical, high-performing, and sustainable public organizations.

Systems doi: 10.3390/systems14060692

Authors: Maria Carmen Huian Mihaela Curea

E-commerce has reshaped short-term financial management by altering transaction speed, payment structures, and supply chain coordination. This study examines how large publicly listed e-commerce firms, viewed as complex digital business systems, adjusted their working capital policies during and after the COVID-19 shock. The sample is based on the 100 largest e-commerce companies worldwide by market capitalization, as reported by CompaniesMarketCap (February 2026), and is reduced to 76 firms from 23 countries due to data availability, yielding 802 firm-year observations. Firm-level data are obtained from LSEG Datastream, while macroeconomic variables are sourced from the World Bank. The analysis distinguishes between two dimensions of working capital: flow-based operational adjustment, measured by the cash conversion cycle (CCC), and stock-based balance-sheet adjustment, captured by net working capital relative to total assets (WC/TA). Fixed-effects models with firm-clustered standard errors are employed. The results indicate a substantial contraction of the CCC during the pandemic, followed by partial persistence of that contraction rather than a return to pre-pandemic norms. In contrast, WC/TA remains broadly stable during the crisis but declines in the post-pandemic period, suggesting a delayed balance-sheet adjustment. Business-model heterogeneity is not statistically significant, which may reflect a common system-level response across e-commerce firm types. Leverage and supply-chain pressures are associated with working capital intensity (WC/TA), while inflation shapes operate cycle duration (CCC). The findings are consistent with a two-stage adaptive response to systemic disruption.

Systems doi: 10.3390/systems14060691

Authors: Bernard P. Zeigler

Engineering programs increasingly rely on simulation for early design decisions, yet a simulation’s obligations—the specific questions it must answer and observations it must provide—are often left implicit until late in development. The Experimental Frame–System Under Test (EFSUT) methodology addresses this gap by requiring these commitments to be specified explicitly early in system design. EFSUT organizes simulation around four core elements—questions, experimental conditions, models, and results—and defines formal relations that clarify how tests are derived and which models can meaningfully be evaluated. We demonstrate the methodology in two contrasting domains, showing how an informally stated motivating question leads to a structured sequence of experimental conditions and guides model selection and interpretation. Compared with existing approaches to model adequacy and digital-engineering traceability, EFSUT provides a clear, question-driven foundation that links stakeholder intent to model evaluation in a transparent, defensible manner. This approach is particularly valuable for aligning simulation practice in multi-model and system-of-systems contexts. Future work includes the automated derivation of Experimental Frames, integration with digital-thread toolchains, and more broadly, development of a lifecycle-spanning, formalized question-driven framework to support evaluation, optimization, and adaptation where traditional requirement-based methods fall short.

Systems doi: 10.3390/systems14060690

Authors: Jian Cao Wanlin Cui Liping Luo Ganggang Xie

Cold-chain supply systems depend on a sequence of linked production and logistics decisions. In prepared-food cold chains, quality may deteriorate not because one visible failure occurs, but because testing, traceability records, temperature monitoring, and abnormal-condition reporting are gradually weakened under cost pressure. Once such hidden effort reduction accumulates, external disturbances may push the system from strict assurance to weakened governance. To explain this nonlinear process, an asymmetric evolutionary game is built between prepared-food producers and cold-chain logistics providers, each choosing between strict and weakened quality assurance. White Gaussian noise is introduced to represent random operating shocks, and the two-population strategy system is projected onto a system-level quality-governance coordinate, q. This projection is used as a transparent baseline coordinate rather than as an assumption of linear system evolution. The reduced system is then transformed into a stochastic cusp catastrophe model, with a resilience indicator used to measure the distance from critical transition conditions. Numerical simulations show that quality assurance costs and short-term cost-saving benefits move the system toward a weakened-governance basin, whereas external incentives, coordination degree, and credible accountability mechanisms support recovery toward strict collaboration. The framework offers a scenario-based resilience diagnosis approach for identifying threshold effects in cold-chain quality governance. Digital traceability, temperature-data sharing, incentive alignment, and accountability rules are further interpreted as operational innovations that improve resilience and reduce avoidable quality losses in sustainable cold-chain operations.

Systems doi: 10.3390/systems14060689

Authors: Jiamei Liu Helian Xu Yuping Deng Jiayi Yuan

As Chinese manufacturing enterprises became more deeply integrated into global value chains, they faced increasingly restrictive U.S. export controls that limited their access to foreign technologies and critical intermediate inputs. Using firm-level data from Chinese listed manufacturing firms over 2006–2015 and the U.S. Entity List, this paper systematically examines the impact of export controls on China’s export technology complexity and explores the underlying mechanisms. The study shows that U.S. export controls significantly reduce manufacturing enterprises’ export technological complexity. The negative effect is more pronounced among enterprises in eastern China, state-owned enterprises, large enterprises, and enterprises operating in high-technology industries. Mechanism analysis shows that export controls suppress the growth of export technological complexity by increasing transaction costs and disrupting supply chains. Although the disruption of innovation chains may stimulate firms’ indigenous innovation, the overall effect of export controls remains negative. Our findings provide theoretical and practical insights for China’s strategies to respond to export controls, enhance the technology complexity of manufacturing exports, and strengthen its position in the global value chain.

Systems doi: 10.3390/systems14060688

Authors: Hong Yao Xiaoyang Liu

This study examines how organizational information environments shape AI adoption intention in the public sector. Based on survey data from 1068 civil servants across 31 provincial-level jurisdictions in China, it develops and tests a framework incorporating organizational fit, institutional inertia, perceived workload, and organizational learning support. The results show that organizational fit facilitates AI adoption, whereas institutional inertia and perceived workload significantly inhibit it. Organizational learning support exhibits context-dependent moderating effects: it strengthens the positive role of fit and mitigates workload-related resistance, but paradoxically amplifies the negative impact of institutional inertia in highly rigid organizations. Heterogeneity analyses further reveal systematic variations across regions, job types, administrative levels, and position ranks. By conceptualizing AI as an embedded organizational information-processing system, this study contributes to understanding AI adoption dynamics in government organizations and highlights the need for context-sensitive learning strategies.

Systems doi: 10.3390/systems14060687

Authors: Lei Wang Jia Cheng Xuan Jiang Tingqiang Chen

From the perspective of interactions between energy firm behavior and government intervention strategies, this study develops a contagion control model for energy firm debt default risk utilizing cellular automata and complex network theory. This research investigates the spatio-temporal evolution of risk transmission and evaluates the efficacy of various mitigation protocols through computational simulation. The research results indicate that: (1) An escalation in both the transmission likelihood and the rate of immunity decay significantly amplifies the propagation strength of debt default risks. Conversely, the stability of the energy firm network is bolstered as the probabilities of immunity and recovery increase. (2) The contagion intensity for debt default risk is positively correlated with market noise, the risk appetite of energy firms, and their corporate influence. It is negatively correlated with risk awareness, creditworthiness, regulatory intensity, and policy subsidies. Furthermore, it exhibits an inverted U-shaped relationship with investor sentiment. (3) Within the interconnected network of energy firms, risk contagion can be effectively mitigated not only by enhancing risk perception and credit standing but also by guiding risk preference and managing firm influence. Furthermore, the integration and adjustment of government intervention strategies, such as regulatory intensity and policy subsidies, can more efficiently accelerate the eradication of debt default risk among energy firms.

Systems doi: 10.3390/systems14060686

Authors: Yiqing Su Ruyi Yang Jiasheng Ou Lihua Li

The conflict between individual rationality and collective rationality, as revealed by the tragedy of the commons, constitutes the core dilemma of collective action in ecological and environmental governance. The key to resolving this dilemma lies in systematically diagnosing the social–ecological system and identifying the crucial factors that hinder cooperation. However, existing diagnostic frameworks have largely focused on identifying the conditions for collective action formation. Thus, those frameworks establish a diagnostic logic centered on conditional principles, but they neglect the capture of the formation process of collective action. To address this gap, this paper further introduces a procedural principle centered on “squeeze-out → transformation → return” into the traditional diagnostic framework. This is achieved by constructing an integrated analytical framework that combines procedural and conditional principles. This study takes the ecological and environmental governance of the Dawangtan Reservoir in Nanning, Guangxi Zhuang Autonomous Region, China, as a case study. The limitations of the traditional framework in diagnosing complex social–ecological systems are also examined. Further, this study demonstrates how the proposed integrated diagnostic framework enables problem identification and dilemma resolution by capturing the formation process of collective action. This research not only enriches the theoretical understanding of the formation process of collective action in complex contexts, but also helps practitioners more efficiently identify context-specific solutions to collective action problems.

Systems doi: 10.3390/systems14060685

Authors: Zhuoyuan Tang Wei Wei Kai Liang Chi Kin Lam

Social media platforms increasingly function as informal education systems for programming learning, yet the systemic support structures these environments provide remain poorly understood. We analyzed 40,004 comments from programming tutorial videos on a major social media platform (2016–April 2025) to identify patterns of learner support needs at scale. Using BERTopic, we identified twelve discussion themes. We then consolidated these themes into a learner-needs typology based on their dominant support functions: instructional-oriented needs, operational support needs, and knowledge-constructionneeds. We mapped this typology onto the Community of Inquiry (CoI) framework to assess its explanatory coverage. This mapping revealed a critical systemic gap. Operational support needs, covering environment configuration, tool integration, dependency management, and technical troubleshooting, constituted the largest category (44.53% of theme-level discourse), exceeding both knowledge-construction needs (28.42%) and instructional-oriented needs (26.95%). Learners repeatedly described these infrastructure-level challenges as disrupting their attempts to engage with content, execute code for testing ideas, and coordinate with peers, yet these operational readiness needs are not fully specified by CoI’s traditional presences. Social presence did not emerge as a standalone theme at the topic-modeling level; rather, social cues were often embedded within task-oriented troubleshooting. Based on these findings, we propose Technical Presence as a context-sensitive extension to the CoI framework, defined as the extent to which a learning community enables operational readiness through accessible infrastructure support and collaborative troubleshooting. As an infrastructural support condition, Technical Presence supports operational readiness within tool-dependent, practice-based learning: when learners report infrastructure failure, the conditions for enacting instructional design, cognitive inquiry, and peer collaboration are correspondingly weakened. These findings carry implications for content creators, platform developers, and education system designers seeking to strengthen the infrastructural foundations of technology-enhanced learning at scale.

Systems doi: 10.3390/systems14060684

Authors: Anna Vrabelova Zuzana Kotianova

The AS/EN9100 standard represents the primary quality management framework governing aerospace supply chains. However, its implementation remains challenging for small and medium-sized enterprises (SMEs) due to limited resources, fragmented processes, and insufficient integration of digital support mechanisms. Existing studies primarily focus on standard interpretation, certification outcomes, or isolated implementation practices, while lacking a structured process–chronological implementation architecture suitable for SME environments. This study develops and empirically validates a digitally supported AS/EN9100 implementation framework using a Design Science Research (DSR) approach combined with Action Research principles. The proposed framework transforms the traditional clause-based interpretation of the standard into a coordinated implementation architecture integrating process management principles, risk-based thinking, and a digital support layer. The framework was validated in a real organizational environment through implementation. The integrated digital support environment also improved the coordination of responsibilities, monitoring of implementation milestones, and management of documentation workflows. From a systems perspective, the study conceptualizes quality management implementation as a socio-technical transformation process rather than a compliance-driven activity. The contribution of the study lies in the development of a transferable organizational and process innovation artifact that integrates process structuring, digital coordination, and adaptive management principles into a unified implementation framework for regulated SME environments.

Systems doi: 10.3390/systems14060683

Authors: Botao Chen Weiwei Hu

The Easterlin paradox and recent distributional reassessments suggest that average effects obscure how subjective disadvantage is generated and reproduced over time. We propose the Social Comparison System (SCS), a framework that represents subjective well-being (SWB) as an internal state and relative income rank as an external conditioning variable within a feedback structure, with three structural properties: threshold activation, state-dependent gain, and rank-conditioned attractor displacement. The properties are recovered through a sample-isolated three-stage framework integrating tree-based machine learning, forest-based heterogeneity estimation, panel-data estimation, and hierarchical Bayesian Markov modeling on a balanced four-wave panel of the China Family Panel Studies (CFPS; 8099 individuals; 32,396 person-wave observations). Stage 1 locates a discrete predictive discontinuity in relative income rank between rank 2 and rank 3 (SHAP jump = 0.383, permutation p < 0.001). Stage 2 carries this boundary into a disjoint validation panel and recovers a negative rank-by-prior-SWB interaction (β = −0.036) and a 2.30-fold larger conditional effect in low- than in high-prior-SWB strata. Stage 3 recovers a 22.6-percentage-point gap in the rank-conditioned occupancy of the lowest within-wave SWB quartile between low- and high-rank subsystems, which under a first-order Markov approximation corresponds to a long-run stationary gap, robust to alternative state-space discretizations. Throughout this paper, relative income rank is treated as a conditioning variable, and the rank-conditioned patterns are interpreted as associational; the long-run quantities are reported under a first-order dynamical approximation rather than as identified causal or fully validated long-run effects. Persistent subjective disadvantage is therefore characterized by unequal dynamics of activation, amplification, and escape, rather than by unequal resources alone. This reframing provides a methodological template for identifying rank-conditioned feedback structures in social-systems data.

Systems doi: 10.3390/systems14060682

Authors: Ben Zhang Runzhe Zhang

Firms can gain a competitive advantage through a strategic patent portfolio, wherein patents elucidate technological advancements and establish legal barriers that keep competitors out. However, patents do not provide a perpetual monopoly within the prevailing open innovation paradigm, which means that firms should keep up with innovation input and patent applications to preserve their market dominance. Fostering technological breakthroughs in the patent network thus becomes a critical issue. Anchored in the theoretical views of open innovation, this study conducts an empirical analysis of patent data to examine how patent network structural features influence the technologies’ breakthrough tendency in the field of autonomous driving (AD). The findings indicate that centrality metrics such as degree centrality, harmonic centrality, and betweenness centrality within AD patent networks exert significant influence on technological breakthrough tendency, and the patent family size plays a moderating role in these relationships. Moreover, this research advances theoretical insights for patent strategy formulation in emerging firms of AD, with broader implications for other technology-intensive sectors.

Systems doi: 10.3390/systems14060681

Authors: Shuxian Zhao Shanshan Zhao Xueli Tan Dongphil Chun Yanfeng Liu

The maritime and logistics sector is undergoing digital transformation, positioning digital logistics platforms (DLPs) as important tools for improving operational coordination, information visibility, and organizational performance (OP). However, prior studies have mainly examined platform adoption, digital capabilities, or macro-level performance outcomes, while paying insufficient attention to the micro-level cognitive and experiential mechanisms through which DLP innovation diffusion is translated into OP, particularly in the Chinese maritime logistics context. Grounded in an integrated framework combining the Stimulus–Organism–Response (SOR) paradigm, Diffusion of Innovations Theory (IDT), and the Extended Technology Acceptance Model (ETAM), this study investigates how DLP innovation diffusion affects OP through perceived usefulness (PU), perceived ease of use (PEOU), and flow experience (FE). Using survey data from 400 professionals in Chinese maritime and logistics enterprises and second-order structural equation modeling (SEM), the results show that DLPs’ innovation diffusion significantly enhances PU, PEOU, and FE. PU has the strongest standardized effect among the paths from DLPs’ innovation diffusion to the mediators (β = 0.779), whereas FE has the strongest direct effect on OP (β = 0.279) and the largest mediating effect. These findings clarify the cognitive–experiential pathway linking DLPs’ innovation diffusion to OP and inform DLPs’ implementation in maritime logistics.

Systems doi: 10.3390/systems14060680

Authors: Handong Tang Dan Wang Henry J. Liu Jianfeng Zhao

Information infrastructure is essential for digital transformation and AI-enabled services, but its operation also involves high electricity consumption and carbon emissions. This study develops a tripartite evolutionary game model involving the government, information-infrastructure operators and the public, and integrates it with system dynamics to examine how regulatory mechanisms influence operators’ net-zero behaviours. The model focuses on operational-stage information infrastructure. Initial parameters are calibrated using the 2023 China Statistical Yearbook on Resources and Environment and expert consultation, with key variables measured by operational revenue, net-zero costs, regulatory costs, incentives, penalties, public scrutiny costs and environmental losses. The results show that operators’ net-zero behaviours may fluctuate under weak or static regulation. Government incentives, penalties and public scrutiny can promote net-zero operations, while dynamic reward–penalty mechanisms are more effective in stabilising behavioural evolution. This study extends evolutionary game theory and system dynamics to the net-zero governance of information infrastructure and provides an adaptive regulatory framework for coordinating government regulation, operator behaviour and public participation.

Systems doi: 10.3390/systems14060679

Authors: Filiz Mizrak Gonca Reyhan Akkartal

Artificial intelligence is reshaping warehouse work through algorithmic task allocation, scanner-based monitoring, KPI feedback, dynamic scheduling, and real-time performance control. Although these systems can improve coordination and operational visibility, they also create governance risks related to fairness, transparency, autonomy, privacy, workload pressure, trust, and employee resistance. This study develops a human-centered decision framework for prioritizing algorithmic management governance packages in third-party logistics (3PL) warehousing. The main contribution is to translate employee-level governance concerns into a scenario-sensitive decision model that helps managers select appropriate governance packages under different operational pressures. The study uses survey data from 380 warehouse employees to examine key psychological and behavioral mechanisms, including procedural fairness, transparency, system/information quality, autonomy, privacy concern, workload, trust, acceptance, and resistance/disengagement. These survey-supported constructs are then converted into six governance criteria: procedural fairness, transparency and contestability clarity, system and information quality, autonomy support, privacy boundary governance, and workload protection. A seven-expert panel evaluates five governance packages under three scenarios: peak season surge, labor shortage/high turnover, and audit pressure/compliance scrutiny. Methodologically, the framework combines Dynamic Multi-Facet Fuzzy Sets to capture membership, non-membership, hesitancy, engagement, and resistance; Bayesian Network weighting to reflect dependencies among governance criteria; and PCA-based ranking optimization to generate scenario-specific and robust rankings. Comparative validation with SAW and TOPSIS is also used to assess ranking consistency. The findings show that effective algorithmic management governance is not a fixed compliance solution. Transparency, workload protection, autonomy support, privacy boundary governance, and procedural fairness become more or less important depending on the operational scenario. A2, which combines transparency, workload protection, and autonomy support, emerges as the strongest robust package. A1 performs best under labor shortage/high turnover, while A3 performs best under audit pressure/compliance scrutiny. These results suggest that 3PL warehouses should adopt adaptive governance routines that combine explainability, contestability, workload safeguards, privacy boundaries, and employee voice mechanisms. The study contributes to the literature on AI in socio-technical systems by showing how human, organizational, and ethical concerns can be embedded into an interpretable decision framework for responsible algorithmic management in logistics work environments.

Systems doi: 10.3390/systems14060678

Authors: Igor Kabashkin

Aircraft digital twins are increasingly used for diagnostics, prognostics, and predictive maintenance, but their role as lifecycle-oriented, multi-stakeholder decision-support ecosystems remains insufficiently developed. This paper addresses this gap by proposing a conceptual systems-engineering framework for an aircraft digital twin ecosystem supporting sustainable aviation transport management. The framework integrates physics-based, data-driven, hybrid, probabilistic, and federated modelling approaches and includes a three-layer ecosystem model, formal mathematical representation of aircraft and digital twin lifecycle evolution, federated model updating, lifecycle decision-support scenarios, reference architecture, validation and trustworthiness principles, and a five-level maturity model. Representative aviation industrial cases are used to interpret the framework. The analysis shows that current industrial practice already contains elements of predictive maintenance, fleet analytics, engine health monitoring, and cloud-enabled MRO optimization, but full aircraft-level lifecycle governance, sustainability trade-off analysis, federated validation, and multi-stakeholder decision orchestration remain underdeveloped. The proposed framework positions aircraft digital twins as asset-level instruments for lifecycle planning, coordinated governance, and sustainability-oriented decision support.

Systems doi: 10.3390/systems14060677

Authors: Lars Tasche Frank Straube Timur Lotz

Road freight logistics is one of the most difficult transport segments to decarbonize. In recent years, battery electric trucks and hydrogen fuel cell electric trucks have emerged as the most promising alternatives to conventional heavy-duty vehicles. However, their integration cannot be reduced to a question of vehicle substitution, as it depends on a broader system of conditions. This paper aims to identify and structure the system-determining parameters that influence the use of battery electric trucks and hydrogen fuel cell electric trucks in road freight logistics. To this end, the study applies a systematic literature review, yielding a final sample of 42 publications. The review shows that drive type suitability depends on parameters across four categories: economic, ecological, performance-related, and external. Accordingly, no single factor determines suitability; rather, outcomes emerge from the interaction of multiple conditions. The reviewed literature does not support a universally superior drive technology. Instead, the suitability of battery electric trucks and hydrogen fuel cell electric trucks depends on the specific configuration of the surrounding system. The paper thus provides a structured framework for future comparative assessments in sustainable road freight logistics. The study is embedded in the Research Campus Mobility2Grid, which provides a practice-oriented context for assessing alternative drive technologies in relation to fleet, depot, energy, and logistics-system requirements.

Systems doi: 10.3390/systems14060676

Authors: Thowayeb H. Hassan Amany E. Salem Muhannad Mohammed Alfehaid Mahmoud I. Saleh

The paper presents AI Sentinel, a closed-loop socio-technical approach to monitoring, analyzing, and responding to negative hotel reviews through a combination of big data analytics, natural language processing, and machine learning predictive modeling. A total of 85,178 reviews were analyzed for 80 European hotel properties, with 5665 (mean = 6.54) classified as negative and 79,513 (mean = 9.22) classified as positive. Latent Dirichlet Allocation (LDA) was used to discover topics; Gradient Boosting was used to classify high-risk reviews (AUC = 0.919); and a rule-based engine was employed for routing recovery/delivery of service. This analysis identified ten major complaint areas in guest reviews, with Cleanliness, staff behavior, and room quality accounting for 47.0% of negative comments about hotels and forming the Critical tier of intervention. There are three key theoretical contributions made by this study: (1) establishing operationalization of joint socio-technical optimization in AI-augmented service management; (2) introducing algorithmic service sensing as a time-compression mechanism for recovery workflow; and (3) demonstrating that the integration of unsupervised topic modeling with supervised risk classifications can provide a compounded analytical approach. Managerial consequences include risk prioritization at the portfolio level, the design of specific services to target certain traveler segments, nationality-based recovery threshold levels, and an appropriate governance structure that meets the requirements of the General Data Protection Regulation and the new European Union Artificial Intelligence Act.

Systems doi: 10.3390/systems14060675

Authors: Yijun Wang Pingan Xiang

Using a complex network evolutionary game model, this study examines the effects of policy incentives, certification mechanisms, price premiums, production costs, and neighborhood learning on farmers’ adoption of organic farming technologies. It aims to reveal the dynamic mechanisms of organic farming technology diffusion under subsidy policies and certification mechanisms. Numerical simulations are conducted to analyze the effects of the subsidy rate and the effectiveness of organic certification on the diffusion level of organic farming technologies. The results show that both subsidy policies and certification mechanisms can promote the diffusion of organic farming technologies; however, the effect of subsidy policies is relatively limited, whereas certification mechanisms play a more significant role. Furthermore, the effects of the subsidy rate and certification effectiveness are influenced by factors such as the proportion of consumers with a preference for organic products, increased production costs, and the organic price premium. Under different levels of bounded rationality and strategy updating rules, the combined “subsidy–certification” policy consistently outperforms single-policy scenarios, with certification mechanisms generally exerting a stronger promotional effect than subsidy policies. In addition, the initial adoption proportion and network size also affect the evolutionary outcomes of the system. A higher initial adoption proportion cannot sustain a higher steady-state diffusion level in the long run, while an increase in network size tends to weaken the effectiveness of policy interventions. Finally, this study proposes policy recommendations, including improving certification and market development mechanisms and strengthening information dissemination and technical service systems, thereby providing practical insights for promoting the diffusion of organic farming technologies.

Systems doi: 10.3390/systems14060674

Authors: Qingkai Dang Wenwen Yu Qiyuan Fan

Physical inactivity and household financial fragility are often studied separately, yet households may respond to health and financial shocks through interrelated behavioral, health, and financial processes. This study examines whether physical activity, health capital, and household financial resilience are dynamically associated in China. Using five waves of the China Family Panel Studies, we construct a household-wave panel and multidimensional indices of health capital and financial resilience. We apply lagged household fixed-effects models, dynamic mediation analysis, and panel vector autoregression with impulse response functions and forecast error variance decomposition. The results indicate that physical activity is positively associated with subsequent health capital, health capital positively predicts subsequent household financial resilience, and financial resilience has a smaller but statistically significant association with later physical activity. The mediation results are consistent with health capital serving as a partial transmission channel between physical activity and financial resilience. The PVAR results show persistent cross-variable responses, suggesting modest dynamic interdependence among the three components rather than definitive causal evidence of a strong self-reinforcing system. Heterogeneity analyses suggest that these associations are more pronounced among low-income, older-head, and chronic-risk households. These findings extend health-capital and household finance research by showing that health behavior and financial resilience can be examined as jointly evolving household-level processes. The results suggest that integrated approaches to physical activity promotion and household financial protection may be worth further policy experimentation and evaluation, especially for vulnerable households.

Systems doi: 10.3390/systems14060673

Authors: Ku Liang Hongjing Cui

Firms increasingly face disruptions arising from technological change, supply chain instability, and uncertain policy environments, making enterprise resilience a key concern for both managers and policymakers. As firms operate within interconnected digital and supply chain systems, this study examines whether digital intelligence policy and supply chain coordination policy are jointly associated with enterprise resilience. Using a firm-year panel of Chinese A-share listed companies from 2010 to 2024, we investigate AI innovation pilot zones and supply chain innovation pilots, with a particular focus on whether their coexistence is associated with a complementarity premium. The results suggest that both AI innovation pilot zones and supply chain innovation pilots are positively associated with enterprise resilience. The interaction between the two policies is significantly positive, providing evidence consistent with an additional joint-policy association beyond their separate associations. Dynamic analysis supports the parallel trend assumption and suggests that the estimated complementarity association becomes stronger over time. Mechanism tests provide channel-consistent evidence that joint policy exposure is associated with higher values of the digital-transformation indicator, stronger supply chain coordination, and greater resource reconfiguration. Heterogeneity analysis further suggests that this association is more pronounced among non-state-owned firms, firms in supply-chain-dependent industries, firms located in cities with stronger digital infrastructure, and firms with higher risk exposure. These findings highlight the potential importance of coordinated policy design for supporting firm-level resilience.

Systems doi: 10.3390/systems14060672

Authors: Zeynep Özgüner Metehan Atay Songül Elçi

The rapid digitalization of airport operations has transformed airports into complex data-driven ecosystems, where effective data governance has become a critical challenge. While prior studies have explored big data applications in aviation, limited attention has been given to the interdependent structure of data governance challenges at the airport level. This study proposes a decision-oriented analytical framework integrating the entropy and DEMATEL methods in two sequential stages systematically identify, prioritize, and model the causal interactions among key big data challenges in airport ecosystems. Using Istanbul Airport (IGA) as a case study, an initial, expert-based assessment was conducted to assess nine critical challenges, including data privacy, integration, organizational culture, and regulatory compliance. The results revealed that data privacy and security is not only the most critical factor but also a primary causal driver, influencing multiple downstream challenges such as ethical considerations and regulatory compliance. The findings further demonstrate that technical and organizational barriers are strongly interconnected, requiring sequenced, system-level interventions rather than isolated solutions. By combining objective weighting with causal analysis, this study contributes to the literature by providing a holistic and actionable decision support framework for airport data governance. The proposed approach offers practical insights for airport authorities and policymakers to design more resilient, secure, and data-driven operational environments.

Systems doi: 10.3390/systems14060671

Authors: Abayomi Ogunrinde Carmen De-Pablos-Heredero

Artificial intelligence (AI) is becoming increasingly embedded in the digital infrastructure of local government, creating new opportunities to improve public sector productivity while also influencing systemic risk and organisational resilience across interconnected public systems. As municipalities adopt AI to automate, support, and transform administrative processes, organisational performance becomes more dependent on the reliability of algorithms, the quality of data, effective governance, and coordination among public institutions. These growing interconnections create new vulnerabilities that can spread across public service networks, yet evidence on the productivity, risk, and resilience implications of AI adoption remains fragmented and dispersed across different fields of research. This study develops an integrative conceptual framework that examines the relationship between AI adoption, public sector productivity, systemic risk, and organisational resilience within interconnected sociotechnical systems. Drawing on insights from productivity economics, systems theory, and public governance, the framework positions total factor productivity (TFP) within a broader public value and risk governance perspective. Using the PRISMA 2020 methodology, the study systematically reviews 68 peer reviewed empirical studies published between 2015 and 2025, assessing productivity outcomes, methodological quality, effect sizes, and contextual factors relevant to local government and networked public administration. The findings show that productivity gains associated with AI are strongly influenced by organisational readiness, including digital maturity, workforce capabilities, governance quality, and institutional coordination. While AI has the potential to improve operational efficiency and strengthen adaptive capacity, inadequate readiness can increase systemic risks arising from algorithmic opacity, cybersecurity challenges, data dependence, coordination failures, and disruptions that may spread across interconnected administrative systems. The review also highlights that resilience depends on the ability of public organisations to anticipate, absorb, adapt to, and recover from AI-related disruptions while maintaining the continuity and quality of public services. The study contributes to theory by integrating perspectives from productivity economics, public administration, and systemic risk within a sociotechnical systems framework. It contributes empirically through a comprehensive synthesis of evidence on AI and public sector productivity and methodologically through the application of transparent PRISMA 2020 review procedures. From a practical perspective, the study offers a conceptual measurement framework and policy guidance for municipal decision makers seeking to improve productivity while strengthening resilience and reducing systemic risks in increasingly interconnected public governance systems.

Systems doi: 10.3390/systems14060670

Authors: Shupeng Lyu Ling Yuan Pengfei Zhang Ching-Hung Lee

Amid the increasingly stringent technological blockade imposed by some Western developed countries, and against the backdrop of rapid advances in artificial intelligence (AI) and emerging Industry 5.0 paradigms, enhancing indigenous innovation capabilities and overcoming key technological bottlenecks has become an urgent imperative. Drawing on path dependence and path creation theories, as well as the Stimulus–Organism–Response (SOR) framework, this study develops an analytical framework to examine how technological blockade accelerates indigenous innovation in latecomer countries. Using a multiple-case study design, the proposed framework is examined through two strategic technology domains: generative artificial intelligence and new energy vehicles (NEVs). Data were collected through technical documentation, publicly available interview materials involving key stakeholders, and third-party reports. The findings indicate that technological blockade accelerates the transition from imitative to indigenous innovation in latecomer countries. Further mechanism analysis reveals that the external pressure formation mechanism, endogenous motivation activation mechanism, and innovation behavior transformation mechanism jointly constitute a pressure-driven transformation mechanism. Specifically, technological blockade, as an external stimulus, disrupts the existing path-dependent state of imitative innovation; the blockade-induced pressure activates the endogenous motivation of innovation actors, which is further reinforced under national foundational conditions and policy guidance; and under the combined influence of external pressure and endogenous motivation, innovation actors’ behaviors undergo significant changes, gradually shifting from reliance on external technologies and resources toward indigenous R&D and breakthroughs in key technologies. This process ultimately drives the transition from imitative to indigenous innovation, marking a shift from path dependence to path creation. By demonstrating how technological blockade accelerates the transition of the innovation trajectory, this study offers theoretical insights for latecomer countries facing external technological constraints and provides policy implications for building resilient innovation ecosystems and enhancing technological autonomy in the era of AI-driven industrial transformation.

Systems doi: 10.3390/systems14060669

Authors: Debao Dai Shali Cao Min Zhao

Against the backdrop of deep digital economic integration, the synergistic agglomeration of new quality productive forces (NQPFs) and digital transformation (DT) has become a key engine for regional high-quality development. Based on data from 31 Chinese provinces during 2011–2023, this study measured the synergistic level of NQPF and DT. Using a modified gravity model, we convert attribute data into relational data and analyze driving mechanisms via social network analysis and quadratic assignment procedures. The results show that the synergistic agglomeration network presents club convergence rather than homogeneous dispersion, forming a structure comprising “polar-core absorption, hub transmission, hinterland integration, and peripheral marginalization.” Eastern regions act as net beneficiaries; Guangdong, Fujian, and other hubs become net-spillover brokers; central and western regions achieve element equilibrium, yet traditional industrial bases face a widening digital divide. Targeted policy implications are proposed. This study provides references for breaking regional digital barriers and optimizing the spatial layout of high-quality development.

Systems doi: 10.3390/systems14060668

Authors: Xianbo Wang Kai Wang Qiong Tang Shuigen Hu

As green and sustainable development has become a central policy objective, identifying new drivers of urban green total factor productivity (GTFP) is of growing importance. This study examines whether data-element development is associated with improvements in urban GTFP and whether this relationship varies across different urban contexts. Using balanced panel data for 270 Chinese prefecture-level and above cities from 2011 to 2021, we construct a city-level data-element development index and employ a two-way fixed-effect framework to conduct the empirical analysis. The results show that data-element development is positively associated with urban GTFP, and this finding remains stable across a series of robustness checks. Further mechanism analyses provide evidence consistent with partial mediation through green technology innovation. The moderation analysis indicates that the GTFP-enhancing effect of data-element development is stronger in cities with higher levels of human capital. Heterogeneity analyses show that the positive effect is more pronounced in Eastern cities, higher-tier cities, and cities with stronger environmental regulation. The findings offer system-oriented policy implications for cities seeking to leverage data elements to improve GTFP, emphasizing coordinated governance across data circulation, human capital, green innovation conversion, and environmental regulation under differentiated urban conditions, thereby supporting more effective pathways for urban green productivity improvement.

Systems doi: 10.3390/systems14060667

Authors: Jianbo Tu Mengchen Lu Yanjun Liu

Against the backdrop of artificial intelligence technology deeply empowering the digital development of the manufacturing industry, enterprises can use AI capability as a crucial source to improve their competitiveness and play a key role in promoting high-quality corporate development. Although the existing literature has revealed the effect of AI capability on organizational performance or other factors, in-depth research remains insufficient regarding whether AI capability can effectively improve firm competitiveness through organizational learning and organizational resilience. Drawing on the resource-based view (RBV), this study constructs a relational model linking AI capability to firm competitiveness via organizational learning and organizational resilience, alongside an investigation into the moderating effect of digital innovation. Using questionnaire surveys of Chinese manufacturing firms, we obtained 304 valid samples. Regression analysis was used to analyze the effect of AI capability on firm competitiveness via organizational learning and organizational resilience. The process-bootstrap method was used to examine the sequential mediating effects of organizational learning and organizational resilience. The results show that AI capability has a direct effect on firm competitiveness, and can also influence firm competitiveness via organizational learning and organizational resilience. AI capability affects firm competitiveness sequentially through organizational learning and organizational resilience. The correlation between exploratory learning and organizational resilience gets moderated by digital innovation. Meanwhile, digital innovation presents a moderated mediating effect on the relations between AI capability and firm competitiveness through exploratory learning and organizational resilience. This paper empirically reveals the “capability-learning-resilience” mechanism through which AI capability affects firm competitiveness, thus further supplementing the study on the effect factors of firm competitiveness. The findings provide theoretical implications for manufacturing enterprises to strategically develop AI capability, implement organizational learning, actively cultivate organizational resilience, and integrate digital innovation to further enhance firm competitiveness.

Systems doi: 10.3390/systems14060666

Authors: Kongting Lee Dirk Pons Malcolm Taylor Anna Earl Yilei Zhang

Industrial systems such as lean practices, quality systems, workplace safety, and organisational culture are often managed as separate systems; however, in practice, they are interdependent. This study presents a preliminary survey instrument (CiE II) to assess organisational conditions commonly associated with effectiveness in manufacturing systems. A multi-stage refinement process was applied to an initial 107-item survey using pilot data (n = 127) collected from engineering students with work-integrated industry experience. The methodology combined exploratory factor analysis, item response theory, and thematic analysis to improve both statistical and conceptual coherence. The resulting instrument comprised 28 items, making it more suitable for industrial deployment. Analysis of responses (N = 127) identified three common facets that support lean, quality, safety, and culture. These are (i) Integrated Quality and Workflow Management (α = 0.960), referring to workers perceptions that quality standards exist and that they are resourced to meet them; (ii) Safe and Collaborative Work Culture (α = 0.901), referring to perceptions of behavioural norms and that workers will be treated fairly within the team; (iii) Supportive Leadership and Professional Growth (α = 0.852), referring to perceptions that management supports workers’ ongoing professional development. The potential benefit is the provision of a candidate survey that economically covers four key domains of relevance for manufacturing organisations. This has the potential to allow cross-domain correlations and larger-span regression models that integrate the four domains.

Systems doi: 10.3390/systems14060664

Authors: Joaquim Jorge Vicente

This paper presents a decision-support framework for analysing the trade-off between total operational cost and customer service level in multi echelon inventory systems. The model integrates fixed-order-quantity replenishment policies, lead-time dynamics and multi objective optimisation to generate a detailed Pareto frontier of efficient solutions. A real multi echelon distribution network is used to demonstrate the model’s applicability and managerial relevance. The results indicate that raising the service level from 46% to the sector standard of 96% increases total cost by approximately 19%, while achieving full demand satisfaction requires an additional 5% cost increase for only marginal service improvement. This pattern reveals a clear cost–service turning point around the 96% service level, beyond which additional gains exhibit sharply diminishing returns. The framework, therefore, provides a transparent and analytical mechanism for identifying replenishment strategies that balance cost efficiency with service performance. By decomposing total cost into ordering, holding, transport and lost-sales components, the model enhances managerial visibility and supports targeted policy adjustments. The paper also discusses limitations of the current formulation and outlines avenues for future research, including alternative replenishment policies, multi-product extensions and richer uncertainty modelling.

Systems doi: 10.3390/systems14060665

Authors: Zhenzhao Li Lingmei Fu Chanyuan Wu Kunqiang Zhao Qing Yang

The optoelectronic information industry is a strategic high-tech industry with wide applications. Compared with developed countries, China’s optoelectronic information industry presents a situation of “strong application and weak technology”. Evaluating the innovation capability of the optoelectronic information industry is the foundation for making scientific development plans. This study provides a methodology for evaluating the provincial innovation capability of the optoelectronic information industry to guide its high-quality development. This article applies multi-attribute utility theory to study the evaluation index system and comprehensive evaluation of the innovation capacity of China’s provincial optoelectronic information industry. Through extensive data collection and matching relationship analysis, an evaluation index system with both sequential decomposition and hierarchical interleaved structure was established, which includes four dimensions and 20 underlying indicators. To better reflect the gap in innovation capability across different provinces, a scientific piecewise non-zero nonlinear utility function model was established. According to the matching relationship between the subsystems and the underlying indicators of innovation capability, a weighted arithmetic mean comprehensive evaluation index model of innovation capability was developed. An empirical study of the optoelectronic information industry’s innovation capability in typical Chinese provinces was conducted using this comprehensive evaluation index model. The results show that Guangdong Province, Beijing Municipality, Jiangsu Province, Zhejiang Province, and Shandong Province ranked in the top five. The innovation capability of China’s optoelectronic information industry needs to be enhanced by strengthening the development of the investment mechanism, optimizing product development and promotion, improving the efficiency improvement mechanism, and solidifying the environmental support system.

Systems doi: 10.3390/systems14060663

Authors: Stoenoiu Carmen Elena Şerban Florica Mioara

The transition to a circular economy (CE) represents a complex socio-technical evolution, requiring synchronized policy frameworks and strategic capital reallocation. Adopting a systems-thinking lens, this study combines bibliometric network mapping with exploratory econometric modelling, to examine the associations between five core policy instruments and tangible circular investments (INV_CE) across the EU-27. Bibliometric analysis identifies the “firm” and “supply chain” as central functional hubs within the CE knowledge system, acting as primary mediators for capital flows. Econometric results indicate that Tradable Permits (TPOs) and an integrated Policy Integration Index (PII), comprising subsidies and energy-based taxes, show the strongest statistical association with circular investment patterns (p ≤ 0.001). However, patterns of structural disparity emerge between OECD and non-OECD Member States (p = 0.014), where the latter often exhibit a more rigid, tax-centric approach. Spearman correlations point toward institutional maturity, specifically government effectiveness (rs = 0.48) and eco-innovation capacity, as a potential systemic gateway for investment absorption. Furthermore, a structural decoupling appears between voluntary approaches (VAs) and governance capacity in emerging systems, suggesting that such instruments may be less effective without “institutional readiness.” The findings suggest that circular transition is path-dependent and congruent with the co-evolution of policy and institutional regimes. To bridge the investment gap, the study highlights the need for systemic interventions that move beyond “one-size-fits-all” regulations toward targeted strategies that strengthen the institutional and data reporting infrastructures of circular systems.

Systems doi: 10.3390/systems14060662

Authors: Yuxin Dai Jiaxin Suo Jinpei Li Di Yao

Port integration reforms constitute an important institutional arrangement for promoting green development in the shipping sector and achieving China’s carbon peaking and carbon neutrality goals. Assessing their carbon-mitigation effect is crucial for improving the governance framework of port integration and promoting high-quality port development. Using panel data for 55 Chinese port cities from 2011 to 2022, this study exploits the staggered implementation of port integration strategies as a quasi-natural experiment and applies a multi-period Difference-in-Differences (DID) approach to estimate their effects on port carbon emissions. The results indicate that port integration reforms significantly reduce carbon emissions, implying that integration acts as a substantive driver for low-carbon transformation. Heterogeneity analysis shows that the emission-abatement effect is stronger for ports outside the Yangtze River Economic Belt, coastal and southern ports, large-scale and small-scale ports, regions with weaker environmental regulation, cities with more advanced industrial structures, and major national hub ports. In contrast, the policy effect is relatively muted for medium-sized ports, highly regulated regions, cities with less advanced industrial structures, and non-core hub ports, where port integration delivers merely weak marginal emission reduction effects. Further mechanism tests reveal that green technological innovation plays a certain mediating role. This study contributes to the literature by providing dynamic causal evidence on how port governance reforms shape green development outcomes. It also offers policy implications for designing differentiated port integration strategies that align with regional development conditions and national low-carbon transition objectives.

Systems doi: 10.3390/systems14060661

Authors: Jue Lai Xiaowei Gong Yu-Peng Zhu

As AI permeates daily life, algorithmic platforms increasingly function as complex sociotechnical systems that shape public perception and societal attitudes. Addressing concerns that AI text-to-image models and search engines reinforce stereotypes, this study focuses on China, a context marked by traditional gender norms and a vast technological ecosystem, examining how algorithmic systems perpetuate gender power structures through occupational representations. Using algorithmic audits of 60 occupations, Z-tests, and QAP network analysis, this study compares platform gender representations with national census data, systematically distinguishing “generative bias” in AI platforms (Doubao Seedream 3.0, Jimeng Image 3.0) from “retrieval bias” in search engines (Baidu, Sogou). Findings reveal that search engines reinforce stereotypes by over-representing dominant genders and obscuring non-mainstream ones. Generative AI exhibits more radical distortions. The specialized AI Jimeng shows a strong gender polarization feature, while the general AI Doubao shows an ideal balanced gender presentation tendency, balancing representation yet creating an equally false reality. Compared to search engines, AI platforms have greater creativity in representing occupational gender. This study reveals a mutually reinforcing bias cycle among audiences, media, and algorithms, offering a crucial non-Western perspective for feminist technology studies and significant implications for equitable AI governance.

Systems doi: 10.3390/systems14060660

Authors: Jiayi Zu Ke Sun Songlin Chen

For the design and production of complex systems, manufacturers are increasingly favoring design outsourcing to obtain external expertise and innovation. The inherent risk in design outsourcing, unlike traditional production outsourcing that caters to detailed specifications, stems from its incomplete contracting nature. This paper focuses on the transaction risk between customers and design contractors, which hampers both parties from benefitting from the contract and obtaining mutual benefits. Prototyping, commonly used for eliciting customer requirements and estimating manufacturing costs, is interpreted in this paper as a means of risk reduction and modeled via a Bayesian estimation process. A quantitative risk model is subsequently developed to investigate the investment decision upon prototyping, taking into consideration the fidelity and cost of the prototype. This paper provides a decision framework for practitioners to understand and manage transaction risks in the design outsourcing of complex systems.

Systems doi: 10.3390/systems14060659

Authors: Nejc Čelik Andrej Škraba

Order picking is one of the most resource-intensive warehouse operations; therefore, improving routing efficiency remains an important challenge. Deep reinforcement learning (DRL) has shown promise in complex optimization problems. However, its application to warehouse order picking is still limited, and graph-based representation learning using graph neural networks (GNNs) in this context remains largely unexplored. This paper proposes a GNN-based DRL method that models warehouse layouts as graphs to optimize order-picking paths while simultaneously learning graph-based structural embeddings of storage locations. The approach is evaluated against exact optimal solutions for smaller instances and against classical heuristic baselines, including the Lin–Kernighan algorithm, in simulated warehouse environments of different scales. The results show that the proposed GNN–DRL approach produces routing solutions with low optimality gaps across different order sizes and remains effective across different warehouse sizes when fine-tuned. In addition, a preliminary small-scale multi-picker experiment illustrates that the proposed framework could be extended toward more complex warehouse optimization settings. Moreover, the learned node embeddings capture meaningful structural properties of warehouse layouts and adapt to different operational contexts, highlighting the potential of integrating GNNs and DRL as a flexible foundation for advanced warehouse optimization.

Systems doi: 10.3390/systems14060657

Authors: Ayodeji Idowu Yemisi Tomilola Babalola

Although small and medium enterprises (SMEs) anchor employment and output across Sub-Saharan Africa, their uptake of artificial intelligence (AI) lags global benchmarks, and prevailing explanations dwell on capital, infrastructure, and institutional voids while overlooking the leadership competencies that determine whether available resources are mobilised at all. Addressing this gap, the present study asks how the digital leadership capabilities of SME owner-managers shape their intention to adopt AI in Nigeria, and through what organisational mechanisms and under what boundary conditions this influence operates. Anchored in the Diffusion of Innovation Theory and the Tigre–Henriques–Curado model of digital leadership, a cross-sectional survey was administered to owner-managers of registered SMEs drawn from six states; a sample of 390 was derived from a population of 23,290 firms using the Taro Yamane formula with proportionate allocation, and 306 valid responses were retained. Partial Least Squares Structural Equation Modelling (WarpPLS 8.0) was applied after confirming reliability (Cronbach’s α: 0.69–0.84; composite reliability: 0.83–0.88), convergent validity (AVE: 0.56–0.67), and common method bias control. Strategic (β = 0.298), interpersonal (β = 0.245), and personal attribute (β = 0.129) capabilities each significantly raised AI adoption intention. In contrast, delivery-related capabilities (β = 0.090, p = 0.057) did not, indicating that pre-adoption intention is governed by cognitive-strategic and relational competencies rather than execution skills. Organisational innovation climate partially transmitted the effects of strategic and interpersonal capabilities, and firm size amplified the interpersonal pathway in medium-sized firms. The study contributes a leadership-centred account of AI adoption in an under-researched African setting and, by estimating mediation and moderation within a single framework, clarifies both why and when digital leadership translates into AI readiness, yielding capability-specific guidance for owner-managers and SME support policy.

Systems doi: 10.3390/systems14060658

Authors: Ali Salem Sarvnaz Baradarani Hasan Yousef Aljuhmani Kolawole Iyiola

Construction projects are increasingly shaped by digital tools such as BIM, IoT-based monitoring, digital twins, and real-time project platforms, yet safety performance remains uneven because these technologies must be interpreted, coordinated, and applied by people. This study examines whether digital leadership is associated with safety performance in construction projects through task- and safety-related employee competence and whether adaptive leadership conditions this relationship. Drawing on Dynamic Capabilities Theory (DCT) and Social Exchange Theory (SET), the study develops a framework in which digital leadership is treated as a leadership capability linked to competence development, while adaptive leadership represents a contextual leadership condition that may strengthen this capability-building process. Data were collected from 487 construction professionals in Türkiye and analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). The results indicate that digital leadership is positively associated with safety performance and task- and safety-related employee competence, and that employee competence is positively associated with safety performance. The indirect relationship between digital leadership and safety performance through employee competence is also significant. Adaptive leadership strengthens the relationship between digital leadership and employee competence and reinforces the conditional indirect effect, although it does not significantly moderate the direct relationship between digital leadership and safety performance. These findings suggest that safer digital project environments depend not only on technology adoption but also on leadership practices that support procedural knowledge, risk awareness, emergency response capability, and adaptation under changing project conditions. The study contributes to research on digital project delivery, construction safety, and leadership by clarifying how technology-oriented leadership and task- and safety-related human capability are associated with safety performance.

Systems doi: 10.3390/systems14060656

Authors: Yuan Hong Fang Huang Jieli Wang Yanhong Feng

Urban land use systems are shaped by market forces, institutional arrangements, and planning regulations. Based on panel data from 266 Chinese prefecture-level cities from 2006 to 2021, this study treats the staggered establishment of environmental courts as a quasi-natural experiment and applies a difference-in-differences approach to examine its impact on urban green land use efficiency. The results indicate that the establishment of environmental courts significantly increases urban green land use efficiency. The effects are more pronounced in cities facing greater environmental pressure, exhibiting more carbon-intensive development patterns, and possessing stronger legal foundations. Mechanism tests suggest that the effect operates primarily through strengthened environmental regulation, enhanced green innovation, and adjustments in local energy consumption structures.

Systems doi: 10.3390/systems14060655

Authors: Joonyong Park

Despite the proliferation of digital integration initiatives, many Small and Medium-sized Enterprises (SMEs) remain trapped in a persistent “Conversion Gap,” where digital adoption fails to manifest as tangible financial performance. Grounded in Resource Conversion Theory, this study anatomizes the structural bottlenecks of this process through a multi-stage Causal AI architecture. Utilizing time-lagged data from 649 SMEs to control for endogeneity, I integrate Gaussian Mixture Modeling (GMM), Tiered Grand-DAG algorithms, and Necessary Condition Analysis (NCA) to decode the non-linear trajectories of value realization. The findings identify a “Low Integration” cohort (34.2%) that fails to translate digital usage into realized outcomes due to a severe deficit in Absorptive Capacity (ACAP). Crucially, NCA diagnostics reveal that ‘perceived usefulness’ serves merely as a necessary baseline condition, whereas ‘user satisfaction’ functions as the primary catalyst for value conversion. Furthermore, multi-group analysis (MGA) confirms that for the most vulnerable SMEs, the causal pathway to revenue is structurally severed (β = 0.000), rendering traditional, linear training interventions ineffective. I propose a fundamental shift toward data-driven, targeted interventions to address these specific structural barriers and facilitate sustainable digital value creation in the SME ecosystem.

Systems doi: 10.3390/systems14060654

Authors: Xue Zhao Bilin Shao Jia Su Xinyu Liu Caiyun Qin Chunhui Liu

Land-use change affects carbon emissions and air quality through coupled ecological and urban processes, yet their coordinated mitigation remains insufficiently understood in heavily industrialized inland regions. Using 113 district-county units in the Fenwei Plain, China, this study developed a regional assessment framework for carbon-PM2.5 co-mitigation under land-use change from 2013 to 2023. The framework integrates dynamic coordination diagnosis, model-estimated improvement-potential assessment, and governance-oriented classification to identify regional differences in coordination status and adjustment capacity. The results showed a phase-wise evolution of the carbon–pollution relationship, with the Synergetic Evolution Index (SEI) shifting toward higher coordination levels in the later period and becoming more stable after 2020. High-synergy areas were concentrated along the Xi’an–Xianyang–Weinan corridor, whereas several peripheral counties remained under combined carbon–pollution pressure. Model-estimated improvement potential showed pronounced spatial heterogeneity and was concentrated mainly in transition zones with moderate coordination levels and relatively strong structural responsiveness. Land-use-related carbon emissions and PM2.5 pollution were the dominant constraints on responsiveness, whereas ecological conditions and urban expansion played secondary roles. By integrating coordination status, model-estimated improvement potential, and structural responsiveness, four governance-relevant types were identified: consolidation areas, transformation areas, ecological mismatch areas, and urbanization lock-in areas. This study provides a regionally differentiated framework for diagnosing carbon–pollution co-mitigation under land-use change and supports adaptive environmental governance in industrializing inland regions.

Systems doi: 10.3390/systems14060653

Authors: Chen Li Kai Yao Xinyue Yan Xinyi Huang

This study examines how configurations of innovation ecosystem functions are associated with dual performance outcomes in global science cities. Moving beyond dominant variable-centered approaches, the study adopts a configurational perspective to explore how interdependent ecosystem conditions jointly shape (1) innovation capacity and (2) innovation-driven entrepreneurial dynamism. Drawing on an original dataset of 200 global science cities in 2023, the study employs fuzzy-set qualitative comparative analysis (fsQCA) to identify ecosystem configurations associated with these outcomes. The findings reveal pronounced equifinality and configurational asymmetry. High innovation capacity is associated with two distinct ecosystem arrangements: an endogenous knowledge-production configuration characterized by the alignment of human capital, research institutions, and industrial actors, and an openness-enabled configuration in which talent mobility co-occurs with institutional and industrial support. By contrast, high innovation-driven entrepreneurial dynamism is more consistently associated with configurations combining endogenous knowledge capacity with conditions related to commercialization and scaling, including talent circulation or advanced computing infrastructure. Configurations associated with non-high outcomes further suggest that underperformance is linked not only to resource deficiencies but also to misalignment across ecosystem domains. The study contributes to innovation ecosystem research by demonstrating that performance differences among global science cities are associated with internally coherent combinations of ecosystem functions rather than the independent effects of isolated factors. More broadly, the findings suggest that innovation capacity and entrepreneurial scaling follow partially different configurational logics, highlighting the importance of complementarity and context-specific ecosystem arrangements in global science cities.

Systems doi: 10.3390/systems14060652

Authors: Jian Yao Junda Wu Haoyuan Feng Jiajing Sun

Supply chain disruptions have become a major source of macro-financial stress, yet their implications for green finance remain underexplored. This paper investigates the state-dependent connectedness between supply-side bottlenecks and the green finance market, represented by clean energy equities, green bonds, and carbon prices. Using daily data on regional Supply Bottleneck Indices (SBIs) for China, the United States, and the euro area, we first construct a global Supply Bottleneck Index (GSBI) by principal component analysis and then estimate pairwise quantile-on-quantile connectedness (QQC) between supply bottleneck indicators and each green finance submarket. The results show that connectedness is strongly nonlinear, asymmetric, and time-varying. For the global indicator, connectedness intensifies at both joint and cross-tail quantile combinations, while mid-quantile states exhibit weak coupling. Regional results reveal clear heterogeneity: China and the United States display the strongest connectedness with clean energy equities in extreme upper-tail states, whereas the euro-area indicator is most tightly linked with the carbon market. Across many extreme states, supply bottleneck indicators show positive net connectedness with green finance markets, but green finance markets, especially carbon prices, can dominate the bilateral connectedness relation under calmer or intermediate regimes. Robustness checks based on average and quantile-rank GSBI constructions, a post-2023 subsample, and alternative QQC tuning choices support the tail-dominance pattern. These findings suggest that supply bottlenecks are not uniformly related to all green assets; rather, they are associated with state-dependent changes in the internal connectedness architecture of the green finance system. The paper contributes to the literature on financial connectedness and sustainable finance by showing how a real-economy disturbance is associated with changes in the connectedness and resilience of green financial markets.

Systems doi: 10.3390/systems14060651

Authors: Maroua Zalfani Edit Süle Mohamad Bakar

Human-Centered Artificial Intelligence (HCAI) has emerged as a promising paradigm to increase transparency, usability, and trust in AI-driven Decision Support Systems (DSS). However, existing research lacks technically detailed accounts of how HCAI principles can be operationalized, implemented, and empirically validated in real decision environments. This study proposes a technically grounded HCAI-oriented DSS framework and presents a concrete prototype implemented in two high-stakes domains: clinical decision support and financial risk assessment. The architecture integrates interpretable machine learning models, SHAP-based explanations, structured user-feedback loops, and governance mechanisms aligned with the EU Trustworthy AI Guidelines. We trained and evaluated domain-specific models using publicly available medical and financial datasets, describing all data preprocessing, model selection, and hyperparameter settings to ensure reproducibility. An empirical study involving 30 domain experts (15 clinicians, 15 financial analysts) compared the HCAI-DSS with a functionally identical black-box DSS. Statistical analyses (paired t-tests with 95% confidence intervals and Cohen’s d) revealed that the HCAI-DSS significantly improved trust (d = 1.23), transparency and understanding (+1.76 mean difference), usability (SUS difference = +15.4), and decision accuracy (+10.2%), without a significant increase in decision time (p = 0.08). Qualitative feedback further demonstrated that explanations, control, and human-in-the-loop features increased confidence and reduced uncertainty. The results provide empirical evidence that HCAI principles tangibly enhance DSS effectiveness and user acceptance. The study contributes (1) a reproducible technical implementation, (2) a validated HCAI-DSS architecture, and (3) multi-domain evidence of improved decision quality. These findings support sustainable and trustworthy AI adoption across sectors and align with emerging regulatory frameworks such as the EU AI Act.

Systems doi: 10.3390/systems14060650

Authors: Yu Chen Zhening Liu Yangzhen Zhao Qihao Zhou Yan Li Weiyi Long Wei Wang

During the early stages of autonomous vehicle (AV) adoption, traditional human-driven vehicles (HVs) and AVs will share urban roads—potentially diminishing the capacity benefits of AVs; thus, dedicated infrastructure strategies, such as AV-exclusive lanes and AV/Toll (AVT) lanes, have been proposed in the literature. While these approaches enhance overall travel efficiency in mixed traffic networks, they often neglect social equity concerns. In particular, the benefits of dedicated infrastructure are largely felt by AV users, while HV users experience a disproportionate increase in equilibrium travel time, negatively impacting social equity. This study optimizes AVT lane toll rates to balance efficiency and equity, ensuring a fair distribution of transportation impacts across user groups. New measurement formulas are introduced to quantify spatial and social equity based on disparities in generalized equilibrium travel costs across different origin–destination pairs and travel modes after an AVT tolling scheme. An equitable AVT tolling model, grounded in fuzzy utility theory, is developed, and a numerical example demonstrates its effectiveness in addressing spatial and social equity concerns in AVT lane tolling contexts.

Systems doi: 10.3390/systems14060649

Authors: Mingyun Gao Qinzi Xiao Ruiping Wu Lixin Xia

Growing awareness of low-carbon environmental protection necessitates incorporating the dynamic evolution of consumer preferences into supply chain management. Unlike prior research focusing on single financing or static preferences, this paper uniquely integrates financing choice under mixed-financing conditions and government incentives under dynamic information updating. We investigate a low-carbon supply chain where a capital-constrained retailer combines low-carbon credit financing (LCF) and trade credit financing (TCF) and under the updating of consumption preferences during the lead time, and build a carbon reduction optimization model. The manufacturer’s abatement and production, the retailer’s financing balancing LCF and TCF under bankruptcy risk, and ordering are modeled within a two-period newsvendor framework with demand information updating. Result analysis reveals the following: (i) Under preference updating and equal subsidy amounts, LCF consistently induces higher carbon reduction efficiency than promotion allowances. (ii) Counterintuitively, promotion allowances dynamically enhance the supply chain’s total profit more effectively than LCF, exposing a critical divergence between environmental efficiency and economic performance. (iii) Across all incentive schemes (single or combined), the marginal gain in carbon reduction efficiency declines with increasing subsidy intensity. These findings imply that policymakers should flexibly align the choice of incentive instrument with the prioritized objective—carbon reduction or supply chain profitability—given the diminishing marginal returns.

Systems doi: 10.3390/systems14060647

Authors: Andreea Ionica Monica Leba

As software systems increasingly combine machine learning, deep learning, and generative AI components with classical deterministic logic, the systematic detection of AI-based algorithmic elements in application code is becoming essential for software audit, compliance with the EU AI Act (Regulation (EU) 2024/1689), and quality assurance. This paper introduces Quality-Informed Neural Networks (QINN), an architecture in which the structured knowledge encoded in the Quality Function Deployment (QFD) House of Quality is embedded into the network topology and weight initialisation through QFD-derived binary structural masks and knowledge-calibrated initialisation—in direct analogy with Physics-Informed Neural Networks (PINNs). The QFD relationship matrices act as structural priors that constrain the hypothesis space toward quality-consistent solutions by enforcing domain-expert-validated sparsity on network connectivity, while an optional QFD-regularised loss term provides an additional soft constraint on the learned weight structure. As a proof of concept, QINN is instantiated in its masked-architecture configuration for the binary classification of software repositories as AI-enabled or classical. On the AIC-199 proof-of-concept dataset, the proposed QINN attains a cross-validated AUC of 99.47% (±1.18%), recall of 100.00% (±0.00%), and F1-score of 99.02% (±1.34%) under QFD-informed structural masking, outperforming a non-learned QFD scoring baseline by 37.37 percentage points in recall and exceeding a cross-validated Random Forest ensemble on AUC by 2.47 percentage points (W = 0, p < 0.05), while producing explanations at three QFD-grounded levels—feature salience, named Technical-Evidence activations, and per-criterion quality requirement scores—that align directly with the EU AI Act documentation obligations. Validation on larger, independently curated datasets and sensitivity analysis of the QFD elicitation process are identified as priorities for future work. A domain-general seven-phase application protocol is provided.

Systems doi: 10.3390/systems14060648

Authors: Hongzhi Wang Zhiyi Wang

In response to increasingly complex risks and challenges and to safeguard national agricultural product supply security, this study constructs a four-dimensional evaluation index system encompassing “Resistance-Adaptation-Recovery-Innovation”. Utilizing panel data from 30 provincial-level regions in China from 2017 to 2023, and employing a comprehensive methodology including the entropy method, Dagum Gini coefficient, Markov chain, kernel density estimation, and convergence models, this research measures the resilience of China’s agricultural product supply chain and investigates its spatiotemporal evolution patterns. The findings are as follows: Firstly, the resilience level of the national agricultural product supply chain shows overall steady improvement, but regional development is uneven, presenting a pattern of eastern regions leading, central regions maintaining steady progress, and western regions catching up. Secondly, the overall resilience difference is strongly correlated with regional variability, with the most pronounced internal disparity observed in the western region. Thirdly, the evolution of resilience exhibits path dependency characterized by the coexistence of a “low-level trap” and “high-level stability”, and less developed regions demonstrate a significant “catch-up effect” towards their more developed counterparts. Based on these findings, this study proposes countermeasures such as implementing targeted policies for different regions, establishing cross-regional coordination mechanisms, strengthening dynamic monitoring and early warning systems, and promoting innovation-driven development and structural upgrading. These efforts aim not only to enhance China’s capacity to respond to risks in its agricultural product supply chain and ensure national food security, but also to provide valuable insights for other countries facing similar challenges in building resilient agricultural systems in an increasingly uncertain global environment.

Systems doi: 10.3390/systems14060646

Authors: Fan Liu Jiaming Liu

Tourist source markets are often described through origin shares or flow volumes, but these measures cannot explain how destination cities build diversified, spatially extended, temporally stable, and network-embedded linkages with source markets. This study develops a system-oriented Market Accessibility and Interaction Index (MAI) for measuring city-level tourist source-market linkage capacity from online review origin data. Using Ctrip reviews collected during a unified data-collection window in October 2025 from 112 A-level attractions selected from the official Liaoning provincial catalog, we compiled a review-level dataset of 76,855 records and retained 29,327 reviews with valid origin, destination-city, timestamp, and distance information for the main analysis. The method proceeded in four steps: sample construction and origin standardization; city-to-origin distance measurement; seven-dimensional indicator construction; and normalization, aggregation, robustness testing, and benchmark comparison. The MAI integrates market scale, source diversity, interprovincial attraction, external effective radius, seasonal stability, input strength, and PageRank centrality. Results reveal pronounced inter-city differentiation. Dalian is the strongest source-market linkage hub, followed by Huludao, Dandong, and Jinzhou, while lower-ranked cities display narrower or less embedded source-market structures. Comparative benchmarking shows that MAI captures multidimensional linkage capacity more comprehensively than single indicators such as review volume, external share, distance reach, or PageRank alone. The study contributes a system-oriented diagnostic measure of city-level source-market linkage capacity and extends online review data from evaluative analysis to structural market measurement.

Systems doi: 10.3390/systems14060645

Authors: Zuo You Shenglan Peng Wei Zhang Hao Tan

Large-scale online reviews provide a valuable source of user feedback, yet existing methods still offer limited support for transforming massive review corpora into structured and decision-relevant requirement knowledge. Rule-based and manually intensive approaches are difficult to scale, while direct end-to-end use of large language models often faces challenges in maintaining stable requirement structures and practical large-scale deployment. To address these limitations, this study proposes a staged framework for mining user requirements from vehicle online reviews, with a particular focus on supporting early-stage requirement engineering. The framework uses large language models for requirement taxonomy construction and automatic annotation and transfers large-scale requirement categorization to a BERT classifier to balance semantic capability with deployment efficiency. A mini-batch iterative strategy is further introduced to progressively induce requirement categories from review data rather than fully predefining them in advance. In addition, sentiment weighting is incorporated to prioritize requirement categories and better reflect user pain points in subsequent analysis and decision support. Experiments on 467,962 review texts show that the proposed method outperforms several conventional machine learning and deep learning baselines on the studied dataset. Beyond quantitative evaluation, the study also examines the structural characteristics of online review-based requirement identification and explores the applicability of the framework in other review scenarios. Overall, the proposed framework provides a practical systems-oriented workflow for large-scale requirement analysis and contributes a review-based approach to early-stage requirement engineering, including requirement identification, categorization, prioritization, and interpretation.

Systems doi: 10.3390/systems14060644

Authors: Shijing Huang Jun Han

Public emergencies are increasingly shared and discussed on short-video platforms, where videos, user interactions, and timing influence how social risks become visible and spread. This study examines a major short-video platform in China and proposes a Multimodal Information Risk Flow (MIRF) framework to understand how risk signals appear, are categorized, and evolve over time during emergencies. We analysed 250,100 anonymized video–comment records collected between 2022 and 2024, combining text, images, audio, user behavior, and author information to study patterns of risk representation and amplification. Our results show that social risk spreads unevenly over time and is strongly influenced by the type of content, with toxic comments leading to faster and larger cascades. General engagement metrics play a smaller role, and shorter times to peak activity are consistently linked to larger risk cascades. These findings highlight that social risk on short-video platforms is multimodal and temporally concentrated, and caution is needed when generalizing results beyond similar platforms and regulatory contexts.

Systems doi: 10.3390/systems14060642

Authors: Alina Panciu Claudiu-Vasile Kifor Marinela Ință Lucian Lobonț Mihai Victor Zerbes

This paper examines the state of the academic literature on the development of after-sales and maintenance services for electric vehicles (EVs), highlighting their critical yet underexplored role in the transition to electrified mobility. Against the backdrop of rising EV sales, this study investigates how service ecosystems influence long-term adoption. A systematic review was conducted to identify recurring themes, barriers, and proposed solutions related to EV maintenance and after-sales systems. The findings indicate that, despite lower mechanical complexity compared to internal combustion vehicles, EVs generate new service demands due to their reliance on electronics, software, and high-voltage systems. Key barriers to EV adoption include high purchase costs, limited charging infrastructure, and shortages of skilled technicians, which collectively affect consumer confidence beyond the point of acquisition. The analysis shows that after-sales services constitute both a technical and economic bottleneck in large-scale EV diffusion. The existing literature predominantly emphasizes theoretical solutions, such as digitalized maintenance and data-driven business models, with limited focus on practical implementation strategies. This paper concludes that sustainable EV adoption depends not only on technological and infrastructural progress but also on workforce adaptation, proposing a transitional management framework to support independent workshops in shifting toward fully electric service operations.

Systems doi: 10.3390/systems14060643

Authors: Haikun Shan Jingya Yang Ji-Na Lee Zhaoqi Li

In the context of AI being deeply embedded in organizational operations, employee work engagement has become a critical mechanism for translating technological potential into realized value. However, existing research offers inconsistent findings on how enterprise AI adoption influences work engagement, highlighting the need for theoretical integration. Drawing on Conservation of Resources and Social Information Processing theories, this study conceptualizes AI adoption as a resource-restructuring mechanism that shapes employees’ resource environments. A dual-path model is proposed, in which AI adoption affects work engagement through a resource gain pathway (fairness perception) and a resource loss pathway (perceived organizational dehumanization). Using three-wave time-lagged data from employees in knowledge-intensive and highly digitalized enterprises in China, the results show that AI adoption simultaneously enhances work engagement via fairness perception and reduces it via perceived dehumanization. Furthermore, AI transparency serves as a key system-level moderator that strengthens the positive pathway while weakening the negative pathway. By integrating resource gain and loss mechanisms, this study provides a system-level explanation of AI’s dual effects and offers insights for balancing technological efficiency with human-centered values in digital transformation.

Systems doi: 10.3390/systems14060641

Authors: Xi Chen Chunai Ma Wanting Wu Fuying Hao

The low-carbon transition is reshaping firms’ strategic behavior and financial resource allocation, yet the mechanisms linking green mergers and acquisitions (green M&A), green credit, and market value remain insufficiently understood. Existing studies recognize the signaling role of environmental actions but often lack a formal game-theoretic framework to explain how green M&A conveys information to financial institutions and capital markets. This study fills this gap by developing a signaling game model between firms and financial institutions to analyze how green M&A affects market value directly and indirectly through credit resource flow. Using panel data of Chinese A-share listed companies from 2013 to 2023, we examine the observable implications derived from the model: the value effect of green M&A, its association with green credit allocation, and the mediating role of green credit. The results show that green M&A is associated with higher market value and greater green credit allocation, while green credit serves as a partial transmission channel. These effects are weakened by internal climate-risk exposure and climate-policy uncertainty, and strengthened by media attention. This study develops a unified theoretical–empirical framework for understanding the economic consequences and financial transmission mechanisms of green M&A, offering implications for corporate green transformation and green-finance resource allocation.

Systems doi: 10.3390/systems14060640

Authors: Yu-Dian Lai Kuei-Hu Chang

A central challenge in complex group decision-making is how to integrate heterogeneous types of information. Experts differ in background and experience, which leads to variation in their understanding of assessment attributes and in the forms of information they provide. Such information may include fuzzy semantic information, fuzzy semantic interval information, and uncertain information, increasing the complexity of the decision process. Traditional approaches commonly employ fuzzy set (FS) and intuitionistic fuzzy set (IFS) models to address group decision-making problems involving human cognitive judgments. These models constrain the sum of the membership degree (MD) and the non-membership degree (non-MD) to be equal to 1 and less than or equal to 1, respectively. However, when assessment information is insufficient, the MD and non-membership degree provided by experts may exceed this constraint. In addition, the score function (SF) and accuracy function (AF) used in FS and IFS do not account for indeterminacy, making them unsuitable for handling incomplete and hesitation information. To overcome these limitations, this study proposes a Pythagorean fuzzy stepwise weight assessment ratio analysis-based method and introduces a new score function (NSF) and a new accuracy function (NAF) within the Pythagorean fuzzy set framework for complex group decision-making. An illustrative case on raw material vendor selection for shipbuilding enterprises is used to validate the effectiveness of the proposed method. The results demonstrate that the method produces more reasonable and accurate vendor ranking outcomes.