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Volume 13
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Systems, Volume 13, Issue 7 (July 2025) – 79 articles

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41 pages, 901 KiB  
Article
Construction of Evaluation Indicator System and Analysis for Low-Carbon Economy Development in Chengdu City of China
by Yan Jia, Yuanyuan Huang, Junyang Zhou and Jushuang Sun
Systems 2025, 13(7), 573; https://doi.org/10.3390/systems13070573 - 11 Jul 2025
Abstract
In order to promote the green and low-carbon transformation of the economy and society, as the economic center of the western region of China, Chengdu actively promotes the national green and low-carbon policies. Some specific measures are proposed to develop Chengdu’s low-carbon economy, [...] Read more.
In order to promote the green and low-carbon transformation of the economy and society, as the economic center of the western region of China, Chengdu actively promotes the national green and low-carbon policies. Some specific measures are proposed to develop Chengdu’s low-carbon economy, such as increasing the ownership of new energy vehicles, promoting the development of park cities and increasing the proportion of clean energy and non-fossil energy, etc. So, in order to accurately evaluate Chengdu’s low-carbon economy-development achievements, firstly, this paper uses literature research to construct an evaluation indicator system for the low-carbon economy development of Chengdu city from five dimensions: economy, energy, technology, environment, and transportation. Then, an improved Analytic Hierarchy Process (AHP) method based on judgment matrices is proposed to determine subjective weights of indicators, while Entropy Weight Method (EWM) and Variation Coefficient (VC) method are used to determine objective weights of the evaluation indicators. Finally, the Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) is used for the multi-indicator comprehensive evaluation of Chengdu’s low-carbon economy development. The evaluation results show that the comprehensive performance of Chengdu’s low-carbon economy has continued to improve from 2018 to 2023, simultaneously, the main influencing factors and weak links are analyzed, and targeted suggestions and strategies for improvement are put forward to promote the low-carbon economy development of Chengdu city. Full article
(This article belongs to the Section Systems Practice in Social Science)
29 pages, 726 KiB  
Article
Research on Investment Decisions and the Coordination of Emission Reduction in the Logistics Service Supply Chain Considering Technical Innovation Output Uncertainty
by Guangsheng Zhang and Zhaomin Zhang
Systems 2025, 13(7), 572; https://doi.org/10.3390/systems13070572 - 11 Jul 2025
Abstract
In the face of economic, social, and environmental pressures, the issue of sustainable development has garnered widespread attention in the Logistics Service Supply Chain (LSSC) with risk attitudes under Technical Output Uncertainty. In this regard, this paper first constructs an optimal emission reduction [...] Read more.
In the face of economic, social, and environmental pressures, the issue of sustainable development has garnered widespread attention in the Logistics Service Supply Chain (LSSC) with risk attitudes under Technical Output Uncertainty. In this regard, this paper first constructs an optimal emission reduction investment game model for an LSSC composed of Logistics Service Integrators (LSIs) and Logistics Service Providers (LSPs) against the backdrop of Technical Output Uncertainty. To this end, it quantifies the participants’ risk attitudes using a mean-variance model to analyze optimal emission reduction investment decisions for centralized and decentralized LSSC under different levels of risk tolerance. Subsequently, it designs a joint contract with altruistic preferences for sharing emission reduction costs in the LSSC. This contract analyzes the parameter constraints for achieving Pareto optimization within the supply chain. Finally, the study employs a case simulation to analyze the changes in expected revenues for centralized LSSC and joint contracts under different risk tolerance levels. The study reveals that (1) in a centralized LSSC, under risk-neutral attitudes, there exists a unique optimal emission reduction investment, which yields the highest expected return from emission reduction. However, under risk-averse attitudes, the expected return is always lower than the optimal expected return under risk neutrality. (2) In a decentralized LSSC, the emission reduction investment decisions of the Logistics Service Providers are similar to those in a centralized LSSC. (3) Under risk-neutral attitudes, the cost-sharing and altruistic preference-based joint contract can also coordinate the risk-averse LSSC under certain constraints, and by adjusting the cost-sharing and altruistic preference parameters, the expected returns can be reasonably allocated. Full article
(This article belongs to the Special Issue Organizational Digital Innovation and Transformation in Enterprise and Government Strategies)
34 pages, 4301 KiB  
Article
A Quasi-Bonjean Method for Computing Performance Elements of Ships Under Arbitrary Attitudes
by Kaige Zhu, Jiao Liu and Yuanqiang Zhang
Systems 2025, 13(7), 571; https://doi.org/10.3390/systems13070571 - 11 Jul 2025
Abstract
Deep-sea navigation represents the future trend of maritime navigation; however, complex seakeeping conditions often lead to unconventional ship attitudes. Conventional calculation methods are insufficient for accurately assessing hull performance under heeled or extreme trim conditions. Drawing inspiration from Bonjean curve principles, this study [...] Read more.
Deep-sea navigation represents the future trend of maritime navigation; however, complex seakeeping conditions often lead to unconventional ship attitudes. Conventional calculation methods are insufficient for accurately assessing hull performance under heeled or extreme trim conditions. Drawing inspiration from Bonjean curve principles, this study proposes a Quasi-Bonjean (QB) method to compute ship performance elements in arbitrary attitudes. Specifically, the QB method first constructs longitudinally distributed hull sections from the Non-Uniform Rational B-Spline (NURBS) surface model, then simulates arbitrary attitudes through dynamic waterplane adjustments, and finally calculates performance elements via sectional integration. Furthermore, an Adaptive Surface Tessellation (AST) method is proposed to optimize longitudinal section distribution by minimizing the number of stations while maintaining high geometric fidelity, thereby enhancing the computational efficiency of the QB method. Comparative experiments reveal that the AST-generated 100-station sections achieve computational precision comparable to 200-station uniform distributions under optimal conditions, and the performance elements calculated by the QB method under multi-attitude conditions meet International Association of Classification Societies accuracy thresholds, particularly excelling in the displacement and vertical center of buoyancy calculations. These findings confirm that the QB method effectively addresses the critical limitations of traditional hydrostatic tables, providing a theoretical foundation for analyzing damaged ship equilibrium and evaluating residual stability. Full article
(This article belongs to the Special Issue AI-Driven Transportation Systems: Innovations, Challenges, and Future Mobility)
39 pages, 4071 KiB  
Article
Research on Optimum Design of Waste Recycling Network for Agricultural Production
by Huabin Wu, Jing Zhang, Yanshu Ji, Yuelong Su and Shumiao Shu
Systems 2025, 13(7), 570; https://doi.org/10.3390/systems13070570 - 11 Jul 2025
Abstract
Agricultural production waste (APW) is characterized by pollution, increasing volume, spatial dispersion, and temporal and spatial variability in its generation. The improper handling of APW poses a growing risk to the environment and public health. This paper focuses on the planning of APW [...] Read more.
Agricultural production waste (APW) is characterized by pollution, increasing volume, spatial dispersion, and temporal and spatial variability in its generation. The improper handling of APW poses a growing risk to the environment and public health. This paper focuses on the planning of APW recycling networks, primarily analyzing the selection of temporary storage sites and treatment facilities, as well as vehicle scheduling and route optimization. First, to minimize the required number of temporary storage sites, a set coverage model was established, and an immune algorithm was used to derive preliminary site selection results. Subsequently, the analytic hierarchy process and fuzzy comprehensive evaluation method were employed to refine and determine the optimal site selection results for recycling treatment facilities. Second, based on the characteristics of APW, with the minimization of recycling transportation costs as the optimization objective, an ant colony algorithm was used to establish a corresponding vehicle scheduling route optimization model, yielding the optimal solution for recycling vehicle scheduling and transportation route optimization. This study not only improved the recycling efficiency of APW but also effectively reduced the recycling costs of APW. Full article
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43 pages, 2590 KiB  
Article
A Study on the Impact of Industrial Robot Applications on Labor Resource Allocation
by Kexu Wu, Zhiwei Tang and Longpeng Zhang
Systems 2025, 13(7), 569; https://doi.org/10.3390/systems13070569 - 11 Jul 2025
Abstract
With the rapid advancement of artificial intelligence and smart manufacturing technologies, the penetration of industrial robots into Chinese markets has profoundly reshaped the structure of the labor market. However, existing studies have largely concentrated on the employment substitution effect and the diffusion path [...] Read more.
With the rapid advancement of artificial intelligence and smart manufacturing technologies, the penetration of industrial robots into Chinese markets has profoundly reshaped the structure of the labor market. However, existing studies have largely concentrated on the employment substitution effect and the diffusion path of these technologies, while systematic analyses of how industrial robots affect labor resource allocation efficiency across different regional and industrial contexts in China remain scarce. In particular, research on the mechanisms and heterogeneity of these effects is still underdeveloped, calling for deeper investigation into their transmission channels and policy implications. Drawing on panel data from 280 prefecture-level cities in China from 2006 to 2023, this paper employs a Bartik-style instrumental variable approach to measure the level of industrial robot penetration and constructs a two-way fixed effects model to assess its impact on urban labor misallocation. Furthermore, the analysis introduces two mediating variables, industrial upgrading and urban innovation capacity, and applies a mediation effect model combined with Bootstrap methods to empirically test the underlying transmission mechanisms. The results reveal that a higher level of industrial robot adoption is significantly associated with a lower degree of labor misallocation, indicating a notable improvement in labor resource allocation efficiency. Heterogeneity analysis shows that this effect is more pronounced in cities outside the Yangtze River Economic Belt, in those experiencing severe population aging, and in areas with a relatively weak manufacturing base. Mechanism tests further indicate that industrial robots indirectly promote labor allocation efficiency by facilitating industrial upgrades and enhancing innovation capacity. However, in the short term, improvements in innovation capacity may temporarily intensify labor mismatch due to structural frictions. Overall, industrial robots not only exert a direct positive impact on the efficiency of urban labor allocation but also indirectly contribute to resource optimization through structural transformation and innovation system development. These findings underscore the need to account for regional disparities and demographic structures when advancing intelligent manufacturing strategies. Policymakers should coordinate the development of vocational training systems and innovation ecosystems to strengthen the dynamic alignment between technological adoption and labor market restructuring, thereby fostering more inclusive and high-quality economic growth. Full article
(This article belongs to the Topic Recent Applications of Artificial Intelligence in Economy and Society)
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22 pages, 1946 KiB  
Article
Exploring the Development Trajectory of Digital Transformation
by Pin-Shin Wang, Tzu-Chuan Chou and Jau-Rong Chen
Systems 2025, 13(7), 568; https://doi.org/10.3390/systems13070568 - 10 Jul 2025
Abstract
Digital transformation (DT) has become a critical focus in both academia and industry. However, its rapid evolution complicates our understanding of its core concepts and developmental patterns. Understanding the development path of DT is crucial for both scholars and practitioners because it provides [...] Read more.
Digital transformation (DT) has become a critical focus in both academia and industry. However, its rapid evolution complicates our understanding of its core concepts and developmental patterns. Understanding the development path of DT is crucial for both scholars and practitioners because it provides a structured view of how the field has progressed over time. This study employs main path analysis (MPA), a citation-based scientometric method, to systematically review and trace the intellectual trajectory of DT research over the past 30 years. Drawing on 1790 academic articles from the Web of Science database, the study identifies key influential works and maps the primary citation paths that shape the field. The analysis reveals three major developmental phases of DT research—engagement, enablement, and enhancement—each characterized by distinct thematic and conceptual shifts. Furthermore, five emerging research trends are uncovered: reinventing digital innovation affordance, value-creation paths of DT, synergistic DT with business and management practices, disciplinary boundaries of DT, and digital leadership. Understanding the intellectual trajectory and emerging trends of DT helps practitioners anticipate technological shifts and align transformation efforts, guiding decision-makers in effectively managing their DT processes. Also, these findings provide a structured framework for understanding the evolution of DT and offer valuable directions for future research in information systems and digital innovation. Full article
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19 pages, 2783 KiB  
Article
Cross-Project Multiclass Classification of EARS-Based Functional Requirements Utilizing Natural Language Processing, Machine Learning, and Deep Learning
by Touseef Tahir, Hamid Jahankhani, Kinza Tasleem and Bilal Hassan
Systems 2025, 13(7), 567; https://doi.org/10.3390/systems13070567 - 10 Jul 2025
Abstract
Software requirements are primarily classified into functional and non-functional requirements. While research has explored automated multiclass classification of non-functional requirements, functional requirements remain largely unexplored. This study addressed that gap by introducing a comprehensive dataset comprising 9529 functional requirements from 315 diverse projects. [...] Read more.
Software requirements are primarily classified into functional and non-functional requirements. While research has explored automated multiclass classification of non-functional requirements, functional requirements remain largely unexplored. This study addressed that gap by introducing a comprehensive dataset comprising 9529 functional requirements from 315 diverse projects. The requirements are classified into five categories: ubiquitous, event-driven, state-driven, unwanted behavior, and optional capabilities. Natural Language Processing (NLP), machine learning (ML), and deep learning (DL) techniques are employed to enable automated classification. All software requirements underwent several procedures, including normalization and feature extraction techniques such as TF-IDF. A series of Machine learning (ML) and deep learning (DL) experiments were conducted to classify subcategories of functional requirements. Among the trained models, the convolutional neural network achieved the highest performance, with an accuracy of 93, followed by the long short-term memory network with an accuracy of 92, outperforming traditional decision-tree-based methods. This work offers a foundation for precise requirement classification tools by providing both the dataset and an automated classification approach. Full article
(This article belongs to the Special Issue Decision Making in Software Project Management)
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23 pages, 633 KiB  
Article
The Effects of Rural Tourism on Rural Collective Action: A Socio-Ecological Systems Perspective
by Yizheng Zhao, Zeqi Liu and Yahua Wang
Systems 2025, 13(7), 566; https://doi.org/10.3390/systems13070566 - 10 Jul 2025
Abstract
Rural tourism has emerged as an efficient strategy for rural revitalization while having various impacts on rural governance. Previous studies predominantly focused on the social implications of rural tourism and its impact on institutional arrangements while neglecting the influence of rural tourism on [...] Read more.
Rural tourism has emerged as an efficient strategy for rural revitalization while having various impacts on rural governance. Previous studies predominantly focused on the social implications of rural tourism and its impact on institutional arrangements while neglecting the influence of rural tourism on collective action in rural governance. This study employed a social–ecological system (SES) framework to investigate the influence of rural tourism on rural collective action, utilizing survey data from 22 provinces (autonomous regions and municipalities directly under the central government), 178 villages, and 3282 rural households across China. The findings revealed that rural tourism exerted a positive influence on collective action, primarily through labor force reflow mechanisms. Specifically, the leadership of village cadres had a moderating role in enhancing this positive correlation. Further analysis revealed significant heterogeneity in tourism governance effects: non-plain regions and villages with medium to low economic development levels exhibited substantial improvements in collective action, whereas plain areas and economically advanced villages may manifest potentially negative impacts. Theoretically, this study contributes to elucidating tourism-driven self-governance mechanisms by applying the SES framework, thereby transcending the traditional dualistic debate between state-market and development-governance paradigms. Practically, we propose institutional designs that embed collective action mechanisms into the coupled synergistic development of rural tourism and community governance, thereby activating endogenous motivations for rural self-governance. Full article
(This article belongs to the Special Issue Systems Thinking: Insights and Solutions to Complex Societal Challenges)
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17 pages, 2576 KiB  
Article
Information Systems in Pre-Combination M&A: Developing an ISOFAM
by Andrej Naraločnik, Andrej Bertoncelj and Tine Bertoncel
Systems 2025, 13(7), 565; https://doi.org/10.3390/systems13070565 - 10 Jul 2025
Abstract
This study develops the Information Systems–Organizational Fit Alignment Model (ISOFAM) to evaluate information systems (IS) alignment during the pre-combination phase of mergers and acquisitions (M&A)—a critical yet underexplored stage in integration planning. Through constructivist grounded theory and the reanalysis of qualitative data from [...] Read more.
This study develops the Information Systems–Organizational Fit Alignment Model (ISOFAM) to evaluate information systems (IS) alignment during the pre-combination phase of mergers and acquisitions (M&A)—a critical yet underexplored stage in integration planning. Through constructivist grounded theory and the reanalysis of qualitative data from two anonymized M&A cases—one domestic (Slovenian) and one cross-border (European)—this study identifies four diagnostic dimensions: Technical Compatibility, Functional Complementarity, Cultural and Governance Fit, and Planning Maturity. ISOFAM is operationalized through visual tools, including the Risk–Opportunity Diagnostic Matrix, IS Misalignment Escalation Flowchart, and Temporal Integration Framework, which facilitate early alignment and strategic foresight. These contributions position IS as a strategic pre-combination priority, enhancing both theoretical and practical outcomes in digital M&A. Full article
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19 pages, 26396 KiB  
Article
Development of a Networked Multi-Participant Driving Simulator with Synchronized EEG and Telemetry for Traffic Research
by Poorendra Ramlall, Ethan Jones and Subhradeep Roy
Systems 2025, 13(7), 564; https://doi.org/10.3390/systems13070564 - 10 Jul 2025
Abstract
This paper presents a multi-participant driving simulation framework designed to support traffic experiments involving the simultaneous collection of vehicle telemetry and cognitive data. The system integrates motion-enabled driving cockpits, high-fidelity steering and pedal systems, immersive visual displays (monitor or virtual reality), and the [...] Read more.
This paper presents a multi-participant driving simulation framework designed to support traffic experiments involving the simultaneous collection of vehicle telemetry and cognitive data. The system integrates motion-enabled driving cockpits, high-fidelity steering and pedal systems, immersive visual displays (monitor or virtual reality), and the Assetto Corsa simulation engine. To capture cognitive states, dry-electrode EEG headsets are used alongside a custom-built software tool that synchronizes EEG signals with vehicle telemetry across multiple drivers. The primary contribution of this work is the development of a modular, scalable, and customizable experimental platform with robust data synchronization, enabling the coordinated collection of neural and telemetry data in multi-driver scenarios. The synchronization software developed through this study is freely available to the research community. This architecture supports the study of human–human interactions by linking driver actions with corresponding neural activity across a range of driving contexts. It provides researchers with a powerful tool to investigate perception, decision-making, and coordination in dynamic, multi-participant traffic environments. Full article
(This article belongs to the Special Issue Modelling and Simulation of Transportation Systems)
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20 pages, 5292 KiB  
Article
Study on the Complexity Evolution of the Aviation Network in China
by Shuolei Zhou, Cheng Li and Shiguo Deng
Systems 2025, 13(7), 563; https://doi.org/10.3390/systems13070563 - 9 Jul 2025
Abstract
As China’s economy grows and travel demand increases, its aviation market has evolved to become the second-largest in the world. This study presents a pioneering analysis of China’s aviation network evolution (1990–2024) by integrating temporal dynamics into a network density matrix theory, addressing [...] Read more.
As China’s economy grows and travel demand increases, its aviation market has evolved to become the second-largest in the world. This study presents a pioneering analysis of China’s aviation network evolution (1990–2024) by integrating temporal dynamics into a network density matrix theory, addressing critical gaps in prior static network analyses. Unlike conventional studies focusing on isolated topological metrics, we introduce a triangulated methodology: ① a network sequence analysis capturing structural shifts in degree distribution, clustering coefficient, and path length; ② novel redundancy–entropy coupling quantifying complexity evolution beyond traditional efficiency metrics; and ③ economic-network coordination modeling with spatial autocorrelation validation. Key innovations reveal previously unrecognized dynamics: ① Time-embedded density matrices (ρ) demonstrate how sparsity balances information propagation efficiency (η) and response diversity, resolving the paradox of functional yet sparse connectivity. ② Redundancy–entropy synergy exposes adaptive trade-offs. Entropy (H) rises 18% (2000–2024), while redundancy (R) rebounds post-2010 (0.25→0.33), reflecting the strategic resilience enhancement after early efficiency-focused phases. ③ Economic-network coupling exhibits strong spatial autocorrelation (Moran’s I>0.16, p<0.05), with eastern China achieving “primary coordination”, while western regions lag due to geographical constraints. The empirical results confirm structural self-organization. Power-law strengthening, route growth exponentially outpacing cities, and clustering (C) rising 16% as the path length (L) increases, validating the hierarchical hub formation. These findings establish aviation networks as dynamically optimized systems where economic policies and topological laws interactively drive evolution, offering a paradigm shift from descriptive to predictive network management. Full article
(This article belongs to the Section Systems Practice in Social Science)
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57 pages, 2043 KiB  
Article
From Transformative Agency to AI Literacy: Profiling Slovenian Technical High School Students Through the Five Big Ideas Lens
by Stanislav Avsec and Denis Rupnik
Systems 2025, 13(7), 562; https://doi.org/10.3390/systems13070562 - 9 Jul 2025
Abstract
The rapid spread of artificial intelligence (AI) in education means that students need to master both AI literacy and personal agency. This study situates a sample of 425 Slovenian secondary technical students within a three-tier framework that maps psychological empowerment onto AI literacy [...] Read more.
The rapid spread of artificial intelligence (AI) in education means that students need to master both AI literacy and personal agency. This study situates a sample of 425 Slovenian secondary technical students within a three-tier framework that maps psychological empowerment onto AI literacy outcomes within a cultural–historical activity system. The agency competence assessments yielded four profiles of student agency, ranging from fully empowered to largely disempowered. The cluster membership explained significant additional variance in AI literacy scores, supporting the additive empowerment model in an AI-rich vocational education and training context. The predictive modeling revealed that while self-efficacy, mastery-oriented motivations, and metacognitive self-regulation contributed uniquely—though small—to improving AI literacy, an unexpectedly negative relationship was identified for internal locus of control and for behavioral self-regulation focused narrowly on routines, with no significant impact observed for grit-like perseverance. These findings underscore the importance of fostering reflective, mastery-based, and self-evaluative learning dispositions over inflexible or solely routine-driven strategies in the development of AI literacy. Addressing these nuanced determinants may also be vital in narrowing AI literacy gaps observed between diverse disciplinary cohorts, as supported by recent multi-dimensional literacy frameworks and disciplinary pathway analyses. Embedding autonomy-supportive, mastery-oriented, student-centered projects and explicit metacognitive training into AI curricula could shift control inward and benefit students with low skills, helping to forge an agency-driven pathway to higher levels of AI literacy among high school students. The most striking and unexpected finding of this study is that students with a strong sense of competence—manifested as high self-efficacy—can achieve foundational AI literacy levels equivalent to those possessing broader, more holistic agentic profiles, suggesting that competence alone may be sufficient for acquiring essential AI knowledge. This challenges prevailing models that emphasize a multidimensional approach to agency and has significant implications for designing targeted interventions and curricula to rapidly build AI literacy in diverse learner populations. Full article
(This article belongs to the Special Issue Generative AI Transformation in Education: Current Issues and Challenges)
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20 pages, 446 KiB  
Article
Green Innovation and Conservative Financial Reporting: Empirical Evidence from U.S. Firms
by Desheng Yin, Xinze Qian, Jason Hu, Zixuan Jiao and Haizhi Wang
Systems 2025, 13(7), 561; https://doi.org/10.3390/systems13070561 - 9 Jul 2025
Abstract
Climate change and environmental degradation necessitate green innovation (GI) to provide new solutions for sustainable economic growth. As many firms allocate scarce resources to green innovation, researchers, practitioners, and policymakers are keen to understand information disclosure on green innovation, particularly in company financial [...] Read more.
Climate change and environmental degradation necessitate green innovation (GI) to provide new solutions for sustainable economic growth. As many firms allocate scarce resources to green innovation, researchers, practitioners, and policymakers are keen to understand information disclosure on green innovation, particularly in company financial statements. This study empirically investigates the relationship between GI and conservative financial reporting. Using a dataset of 8945 unique firms, from 2001 to 2024, we discover a negative relationship between GI and conservative financial reporting. We further document that firms with high exposure to climate change exhibit a more pronounced negative relationship between GI and conservative financial reporting. In addition, we find that the presence of regulatory risks and public awareness, particularly after the adoption of the Paris Agreement, weakens the negative association between GI and conservative financial reporting. Our findings shed further light on information disclosure on green innovation, which is crucial for various stakeholders to utilize such information and make relevant decisions. Full article
(This article belongs to the Special Issue Business Innovation: From Management Systems to Corporate Social Responsibility)
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25 pages, 854 KiB  
Article
The Impact of E-Commerce on Sustainable Development Goals and Economic Growth: A Multidimensional Approach in EU Countries
by Claudiu George Bocean, Adriana Scrioșteanu, Sorina Gîrboveanu, Marius Mitrache, Ionuț-Cosmin Băloi, Adrian Florin Budică-Iacob and Maria Magdalena Criveanu
Systems 2025, 13(7), 560; https://doi.org/10.3390/systems13070560 - 9 Jul 2025
Abstract
In the digital age, e-commerce has become a critical part of modern economies, shaping global economic growth and the pursuit of the Sustainable Development Goals (SDGs). This study uses robust statistical methods to explore the complex relationships between traditional trade, e-commerce, and key [...] Read more.
In the digital age, e-commerce has become a critical part of modern economies, shaping global economic growth and the pursuit of the Sustainable Development Goals (SDGs). This study uses robust statistical methods to explore the complex relationships between traditional trade, e-commerce, and key economic and sustainability indicators. The General Linear Model (GLM), factor analysis, and linear regression reveal that conventional trade remains vital for GDP growth, even though e-commerce clearly influences SDG performance. The study emphasizes the catalytic role of e-commerce in advancing sustainability by showing how treating it as a dependent variable speeds up SDG progress through Brown, Holt, and ARIMA forecasting models. Additionally, cluster analysis uncovers a strong link between higher SDG scores and increased e-commerce activity, with countries scoring better on sustainability often having more companies in the digital economy and earning more online. This research provides a comprehensive understanding of how e-commerce can support global sustainability goals, along with integrated policy recommendations that promote digital transformation and long-term environmental and social resilience. Full article
(This article belongs to the Special Issue Sustainable Business Models and Digital Transformation)
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26 pages, 547 KiB  
Article
Exploring Resilience Through a Systems Lens: Agile Antecedents in Projectified Organizations
by Nuša Širovnik and Igor Vrečko
Systems 2025, 13(7), 559; https://doi.org/10.3390/systems13070559 - 9 Jul 2025
Abstract
As organizations become increasingly projectified, safeguarding the resilience of project professionals and teams emerges as a critical organizational challenge. Adopting a systems lens, we investigate how agile mindsets and agile practices function as systemic antecedents of resilience at the individual and team levels. [...] Read more.
As organizations become increasingly projectified, safeguarding the resilience of project professionals and teams emerges as a critical organizational challenge. Adopting a systems lens, we investigate how agile mindsets and agile practices function as systemic antecedents of resilience at the individual and team levels. Eleven semi-structured interviews with experienced project managers, product owners, and team members from diverse industries were analyzed through inductive thematic coding and system mapping. The findings show that mindset supplies psychological resources—self-efficacy, openness and a learning orientation—while practices such as team autonomy, iterative delivery and transparent communication provide structural routines; together they trigger five interlocking mechanisms: empowerment, fast responsiveness, holistic team dynamics, stakeholder-ecosystem engagement and continuous learning. These mechanisms reinforce one another in feedback loops that boost a project system’s adaptive capacity under volatility. The synergy of mindset and practices is especially valuable in hybrid or traditionally governed projects, where cognitive agility offsets structural rigidity. This study offers the first multi-level, systems-based explanation of agile antecedents of resilience and delivers actionable levers for executives, transformation leaders, project professionals, and HR specialists aiming to sustain talent performance in turbulent contexts. Full article
(This article belongs to the Special Issue Agile Approaches to Organizational Governance: Towards a Resilient and Sustainable System)
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29 pages, 3384 KiB  
Article
Research on Collaborative Governance of Cross-Domain Digital Innovation Ecosystems Based on Evolutionary Game Theory
by Zeyu Tian, Hua Zou, Shuo Yang and Qiang Hou
Systems 2025, 13(7), 558; https://doi.org/10.3390/systems13070558 - 8 Jul 2025
Abstract
The complexities inherent in resource management within cross-domain digital innovation ecosystems have significantly intensified, giving rise to heightened challenges in collaborative interactions among diverse stakeholders, thereby directly impacting systemic stability. Conventional governance frameworks for innovation ecosystems are inadequate in effectively managing the uncertainties [...] Read more.
The complexities inherent in resource management within cross-domain digital innovation ecosystems have significantly intensified, giving rise to heightened challenges in collaborative interactions among diverse stakeholders, thereby directly impacting systemic stability. Conventional governance frameworks for innovation ecosystems are inadequate in effectively managing the uncertainties and risks inherent in these environments. To address the collaborative governance dilemma and enhance governance efficiency, this paper aims to construct an effective collaborative governance mechanism for a cross-domain digital innovation ecosystem and explore the optimal strategy choices of key governance stakeholders, including the government, digital platform enterprises, and other relevant parties. This research utilizes evolutionary game theory to construct a model comprising three governing entities: the government, digital platform enterprises, and stakeholders. It investigates the evolutionary dynamics of collaborative governance strategies among these entities and the factors that influence governance. Following this, a system dynamics methodology is employed for simulation analysis. The results reveal the following: (1) As the initial intentions of the governing entities evolve, governance decisions within the system tend to stabilize, characterized by a strategic combination of proactive regulation, active cooperative governance, and engaged participation. This equilibrium governance strategy significantly fosters the stable advancement of cross-domain digital innovation ecosystems. (2) The punitive measures enacted by the government and the internal incentive structures of the system positively influence the evolution of governance decisions towards collaborative governance. (3) The cost–benefit assessment of the primary governing entity, the digital platform enterprise, demonstrates a detrimental effect on the evolution of governance decisions towards collaborative governance. These findings are vital for refining the collaborative governance frameworks of cross-domain digital innovation ecosystems and for promoting the robust and stable progression of the system. Full article
(This article belongs to the Section Systems Practice in Social Science)
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20 pages, 475 KiB  
Article
Hierarchical Modeling and Analysis of an International Conflict Based on Hesitant Fuzzy Linguistic Term Sets
by Junji Hao, Bingfeng Ge, Yuming Huang, Zeqiang Hou, Tianjiao Yang and Wanying Wei
Systems 2025, 13(7), 557; https://doi.org/10.3390/systems13070557 - 8 Jul 2025
Abstract
In this article, to address the uncertainty of preference information in interrelated conflicts in the real world, a hierarchical conflict modeling and analysis approach based on hesitant fuzzy linguistic term sets (HFLTSs) is proposed. First, considering the hesitancy and fuzziness of decision makers [...] Read more.
In this article, to address the uncertainty of preference information in interrelated conflicts in the real world, a hierarchical conflict modeling and analysis approach based on hesitant fuzzy linguistic term sets (HFLTSs) is proposed. First, considering the hesitancy and fuzziness of decision makers (DMs) when expressing preferences in hierarchical conflicts, a preference representation approach based on HFLTSs is introduced. Building upon hesitant fuzzy linguistic preference, four distinct types of hesitant fuzzy stability definitions of the two-level hierarchical graph model for conflict resolution (HGMCR) are extended, and a corresponding algorithm is developed to solve the global conflict hesitant fuzzy equilibrium states. Finally, this study is applied to investigate the outbreak and development of a specific international conflict, verifying the feasibility and effectiveness of the proposed approach. The hesitant fuzzy equilibrium states of an international conflict indicate that the attitudes of domestic forces reflect a nation’s performance in the warand that the conflict may endure for an extended duration. The hierarchical conflict modeling and analysis approach based on HFLTSs allows DMs to express the hesitation and fuzziness of preferences under uncertainty, facilitates the comprehension of the intrinsic logic behind interactions among DMs at various levels, and enhances the analysis to achieve more foresighted equilibria. Full article
(This article belongs to the Section Systems Practice in Social Science)
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29 pages, 1282 KiB  
Article
The Role of Business Models in Smart-City Waste Management: A Framework for Sustainable Decision-Making
by Silvia Krúpová, Gabriel Koman, Jakub Soviar and Martin Holubčík
Systems 2025, 13(7), 556; https://doi.org/10.3390/systems13070556 - 8 Jul 2025
Abstract
This study addresses the multifaceted challenges inherent in implementing effective smart-city waste-management systems. Recent global trends indicate increased adoption of Industry 4.0 technologies—such as the Internet of Things (IoT), artificial intelligence (AI), and data analytics—to optimize waste collection and processing. The central research [...] Read more.
This study addresses the multifaceted challenges inherent in implementing effective smart-city waste-management systems. Recent global trends indicate increased adoption of Industry 4.0 technologies—such as the Internet of Things (IoT), artificial intelligence (AI), and data analytics—to optimize waste collection and processing. The central research question investigates the role of innovative business models and sustainable decision-making frameworks in advancing smart waste management within urban environments. This research integrates three interrelated domains: business-model innovation, smart-city paradigms, and sustainability in waste management. Its novelty lies in synthesizing these domains, conducting a comparative analysis of best practices from leading European smart cities, and proposing a conceptual framework to guide sustainable decision-making. Methodologically, the study employs a systematic literature review, case-study analyses, and the synthesis of theoretical and empirical data. Key findings demonstrate that innovative business models—such as product-as-a-service, circular-economy approaches, and waste-as-a-service—substantially enhance the sustainability and operational efficiency of urban waste systems. However, many cities lack comprehensive strategies for integrating these models, highlighting the necessity for deliberate planning and active stakeholder engagement. Based on these insights, the study offers actionable recommendations for policymakers and urban managers to embed sustainable business models into smart-city waste infrastructures. These contributions aim to promote the development of resilient, efficient, and environmentally responsible waste-management systems in smart cities. Full article
(This article belongs to the Special Issue Decision-Making in Sustainable Business Models: Prediction and Modeling)
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18 pages, 1222 KiB  
Article
Enhancing Programming Performance, Learning Interest, and Self-Efficacy: The Role of Large Language Models in Middle School Education
by Bixia Tang, Jiarong Liang, Wenshuang Hu and Heng Luo
Systems 2025, 13(7), 555; https://doi.org/10.3390/systems13070555 - 8 Jul 2025
Abstract
Programming education has become increasingly vital within global K–12 curricula, and large language models (LLMs) offer promising solutions to systemic challenges such as limited teacher expertise and insufficient personalized support. Adopting a human-centric and systems-oriented perspective, this study employed a six-week quasi-experimental design [...] Read more.
Programming education has become increasingly vital within global K–12 curricula, and large language models (LLMs) offer promising solutions to systemic challenges such as limited teacher expertise and insufficient personalized support. Adopting a human-centric and systems-oriented perspective, this study employed a six-week quasi-experimental design involving 103 Grade 7 students in China to investigate the effects of instruction supported by the iFLYTEK Spark model. Results showed that the experimental group significantly outperformed the control group in programming performance, cognitive interest, and programming self-efficacy. Beyond these quantitative outcomes, qualitative interviews revealed that LLM-assisted instruction enhanced students’ self-directed learning, a sense of real-time human–machine interaction, and exploratory learning behaviors, forming an intelligent human–AI learning system. These findings underscore the integrative potential of LLMs to support competence, autonomy, and engagement within digital learning systems. This study concludes by discussing the implications for intelligent educational system design and directions for future socio-technical research. Full article
(This article belongs to the Special Issue The Application of a Large Language Model (LLM) in Education Reform and Innovation)
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26 pages, 2310 KiB  
Article
Identification and Forecasting of Key Influencing Factors in China’s Agricultural Carbon Emissions: Based on Machine Learning Method
by Juntong Liu, Xiong Peng, Malan Huang, Yuzhou Ma, Cancan Jiang, Wanling Hu and Jinxin Zhang
Systems 2025, 13(7), 554; https://doi.org/10.3390/systems13070554 - 8 Jul 2025
Viewed by 39
Abstract
Identifying the key factors influencing agricultural carbon emissions and accurately predicting future trends are essential for achieving carbon peak and carbon neutrality goals. This study aims to assess carbon emissions in agriculture from 1997 to 2022, construct an accurate model to identify the [...] Read more.
Identifying the key factors influencing agricultural carbon emissions and accurately predicting future trends are essential for achieving carbon peak and carbon neutrality goals. This study aims to assess carbon emissions in agriculture from 1997 to 2022, construct an accurate model to identify the key influencing factors, and predict carbon emissions in agriculture from 2023 to 2030 with an intelligent prediction system to discuss risk management. Additionally, the Dagum method was employed to explore regional differences in agricultural carbon emissions across China. The results reveal that China’s agricultural carbon emissions exhibited a fluctuating trend from 1997 to 2022, peaking in 2015, followed by a period of decline and a moderate rebound in recent years. Elastic Net Regression identified eleven key variables, including Agricultural Machinery Level (MA), Numbers of Agricultural Tools (AT), and Agricultural Industrial Structure Upgrading (AICE), as major determinants of agricultural carbon emissions. Furthermore, the RF-PSO method demonstrated the highest predictive accuracy, forecasting a minor peak in agricultural carbon emissions in 2027, followed by stabilization. Regionally, imbalances in emissions were observed, with the intensity of transvariation accounting for 37.078% of the disparity. Therefore, the Chinese government is advised to implement region-specific strategies for controlling agricultural carbon emissions, cultivate new high-quality agricultural productivity, and promote advanced technologies. Full article
(This article belongs to the Section Supply Chain Management)
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16 pages, 1305 KiB  
Article
Unveiling Gig Economy Trends via Topic Modeling and Big Data
by Oya Ütük Bayılmış, Serdar Orhan and Cüneyt Bayılmış
Systems 2025, 13(7), 553; https://doi.org/10.3390/systems13070553 - 8 Jul 2025
Viewed by 40
Abstract
The gig economy, driven by flexible and platform-based work, is reshaping labor markets and employment norms. Understanding public perceptions of this shift is critical for promoting social good and informing equitable policy. This study employs big data analytics and Latent Dirichlet Allocation (LDA) [...] Read more.
The gig economy, driven by flexible and platform-based work, is reshaping labor markets and employment norms. Understanding public perceptions of this shift is critical for promoting social good and informing equitable policy. This study employs big data analytics and Latent Dirichlet Allocation (LDA) topic modeling to analyze 15,259 tweets collected from the X platform. Seven key themes emerged from the data, including labor precarity, flexibility, algorithmic control, platform accountability, gender disparities, and worker rights. While some users emphasized autonomy and new income opportunities, most expressed concerns about job insecurity, lack of protections, and digital exploitation. These findings offer real-time insights into how gig work is discussed and contested in public discourse. The study highlights how social media analytics can inform labor policy, guide platform regulation, and support advocacy efforts aimed at building a fairer and more resilient gig economy. Full article
(This article belongs to the Section Artificial Intelligence and Digital Systems Engineering)
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24 pages, 651 KiB  
Article
Security Investment and Pricing Decisions in Competitive Software Markets: Bug Bounty and In-House Strategies
by Netnapha Chamnisampan
Systems 2025, 13(7), 552; https://doi.org/10.3390/systems13070552 - 7 Jul 2025
Viewed by 72
Abstract
In increasingly competitive digital markets, software firms must strategically balance cybersecurity investments and pricing decisions to attract consumers while safeguarding their platforms. This study develops a game-theoretic model in which two competing firms choose among three cybersecurity strategies—no action, bug bounty programs, and [...] Read more.
In increasingly competitive digital markets, software firms must strategically balance cybersecurity investments and pricing decisions to attract consumers while safeguarding their platforms. This study develops a game-theoretic model in which two competing firms choose among three cybersecurity strategies—no action, bug bounty programs, and in-house protection—before setting prices. We demonstrate that cybersecurity efforts and pricing are interdependent: investment choices significantly alter market outcomes by influencing consumer trust and competitive dynamics. Our analysis reveals that a bug bounty program is preferable when consumer sensitivity to security and the probability of ethical vulnerability disclosures are high, while in-house protection becomes optimal when firms must rebuild credibility from a weaker competitive position. Furthermore, initial service quality gaps between firms critically shape both investment intensity and pricing behavior. By jointly endogenizing security efforts and prices, this study offers new insights into strategic cybersecurity management and provides practical guidance for software firms seeking to integrate security initiatives with competitive pricing strategies. Full article
(This article belongs to the Section Systems Practice in Social Science)
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18 pages, 315 KiB  
Article
Digital Transformation and Corporate Innovation in SMEs
by Tao Cen and Shuping Lin
Systems 2025, 13(7), 551; https://doi.org/10.3390/systems13070551 - 7 Jul 2025
Viewed by 78
Abstract
Whether and how digital transformation affects innovation in small and medium-sized enterprises (SMEs) remains to be examined. This study aims to answer this question using a sample of SMEs listed on the Chinese National Equities Exchange and Quotations (NEEQ) market from 2012 to [...] Read more.
Whether and how digital transformation affects innovation in small and medium-sized enterprises (SMEs) remains to be examined. This study aims to answer this question using a sample of SMEs listed on the Chinese National Equities Exchange and Quotations (NEEQ) market from 2012 to 2023. Employing textual mining techniques, this paper measures the degree of digital transformation through keyword frequency analysis of annual reports, while innovation is measured by the number of patent grants. Panel fixed effects models show that digital transformation significantly enhances corporate innovation in SMEs. This relationship remains robust after comprehensive endogeneity and additional robustness tests. Mechanisms analysis reveals that digital transformation alleviates financial constraints and enhances supply chain diversity, enabling SMEs to allocate more resources toward innovation activities. Heterogeneity analysis reveals that the positive effect of digital transformation on innovation is more pronounced for firms located in cities with higher digital finance coverage, in midwestern regions, and in industries with lower digitalization levels. These findings shed light on the power of digital technology, highlighting how its adoption can significantly bolster the innovation capacity of SMEs and drive their growth in a rapidly evolving digital economy. Full article
(This article belongs to the Special Issue Digital Innovation and Business Model Transformation in Small- and Medium-Sized Enterprises)
23 pages, 481 KiB  
Article
Reframing Technostress for Organizational Resilience: The Mediating Role of Techno-Eustress in the Performance of Accounting and Financial Reporting Professionals
by Sibel Fettahoglu and Ibrahim Yikilmaz
Systems 2025, 13(7), 550; https://doi.org/10.3390/systems13070550 - 7 Jul 2025
Viewed by 52
Abstract
This study examines how employees perceive technology-based demands during the digital transformation process and how these perceptions affect job performance. The research utilized data obtained from 388 experts in the accounting and financial reporting profession, a knowledge-intensive field that heavily employs new technologies [...] Read more.
This study examines how employees perceive technology-based demands during the digital transformation process and how these perceptions affect job performance. The research utilized data obtained from 388 experts in the accounting and financial reporting profession, a knowledge-intensive field that heavily employs new technologies (e.g., ERP systems, digital audit tools). The data collected through a convenience sampling method was analyzed using SPSS 27 and SmartPLS 4 software. The findings reveal that the direct effect of technostress on job performance is not significant; however, this stress indirectly contributes to performance through techno-eustress. In this study, techno-eustress refers to the cognitive appraisal of technology-related demands as development-enhancing challenges rather than threats. This concept is theoretically grounded in the broader eustress framework, which views stressors as potentially motivating and growth-promoting when positively interpreted. The model is based on Cognitive Evaluation Theory, the Job Demands–Resources Model, and Self-Determination Theory. This study demonstrates that digital transformation can promote not only operational improvements but also organizational resilience by enhancing employees’ psychological resources and adaptive capacities. By highlighting the mediating role of techno-eustress, this research offers a nuanced perspective on how human-centered cognitive mechanisms can strategically support performance and sustainability in the face of technological disruption—an increasingly relevant area for organizations striving to thrive amid uncertainty. Full article
(This article belongs to the Special Issue Strategic Management Towards Organisational Resilience)
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19 pages, 3586 KiB  
Article
Safety Analysis of Partial Downward Fire Evacuation Mode in Underground Metro Stations Based on Integrated Assessment of Harmful Factors
by Heng Yu, Yijing Huang and Haiyan He
Systems 2025, 13(7), 549; https://doi.org/10.3390/systems13070549 - 7 Jul 2025
Viewed by 98
Abstract
Underground metro stations are integral to urban transit infrastructure, and ensuring their safety during fire emergencies is crucial. This study proposes a novel evacuation strategy for underground metro stations wherein a segment of evacuees descends to the platform level via train, while the [...] Read more.
Underground metro stations are integral to urban transit infrastructure, and ensuring their safety during fire emergencies is crucial. This study proposes a novel evacuation strategy for underground metro stations wherein a segment of evacuees descends to the platform level via train, while the remaining individuals evacuate upward to the ground level through station exits. A novel safety assessment methodology is established to evaluate fire evacuation efficacy, incorporating the cumulative effects of smoke, elevated temperatures, carbon dioxide, and reduced oxygen levels. Employing an actual underground metro station in Guangzhou, China, as a case study, fire and evacuation models were developed to compare the traditional upward evacuation method with the proposed partial downward evacuation strategy. The analysis reveals that both evacuation strategies are effective under the assessed fire scenario. However, the partial downward evacuation is completed more swiftly—in 385.5 s compared to 494.8 s for upward evacuation—thereby mitigating smoke inhalation risks, as the smoke height remains above the critical threshold of 1.8 m for a longer duration than observed in the upward evacuation scenario. Simulations further indicate that neither high temperatures nor carbon monoxide concentrations reach hazardous levels in either evacuation mode, ensuring evacuee safety. The study concludes that, with appropriate training arrangements and under specific fire and evacuation conditions, the partial downward evacuation strategy is safer and more efficient than upward evacuation. Full article
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2 pages, 169 KiB  
Correction
Correction: Sun et al. C2C E-Commerce Platform Trust from the Seller’s Perspective Based on Institutional Trust Theory and Cultural Dimension Theory. Systems 2025, 13, 309
by Yulu Sun, Zhenhua Wang, Hongxiao Lyu and Qixing Qu
Systems 2025, 13(7), 548; https://doi.org/10.3390/systems13070548 - 7 Jul 2025
Viewed by 66
Abstract
In the published publication [...] Full article
26 pages, 954 KiB  
Article
A Framework for Sustainability Performance Measurement Through Process Mining: Integration of GRI Metrics in Operational Processes
by Ourania Areta Hiziroglu and Onur Dogan
Systems 2025, 13(7), 547; https://doi.org/10.3390/systems13070547 - 6 Jul 2025
Viewed by 96
Abstract
Organizations face significant challenges in measuring and enhancing sustainability performance across complex operational processes. Current assessment methods frequently lack granularity, real-time capability, and integration with operational data. This study addresses these gaps by developing a conceptual framework that integrates business process mining with [...] Read more.
Organizations face significant challenges in measuring and enhancing sustainability performance across complex operational processes. Current assessment methods frequently lack granularity, real-time capability, and integration with operational data. This study addresses these gaps by developing a conceptual framework that integrates business process mining with Global Reporting Initiative (GRI) metrics. The methodology incorporates environmental, social, and economic sustainability indicators into process mining techniques through systematic metric mapping and event log enrichment. The framework enables the extraction and analysis of sustainability performance data at the process level, creating detailed heat maps that visualize resource utilization, emissions, and waste generation. An application to a Purchase-to-Pay process case study demonstrates how process variants impact sustainability metrics differently. Delays increase emissions by 16.7%, while rework increases waste generation by 41.7%. The results identify specific process bottlenecks with high environmental impact and reveal critical misalignments between economic and environmental sustainability goals. This framework provides organizations with a standardized yet flexible approach to measuring sustainability performance, bridging the gap between high-level sustainability reporting and operational processes. It enables continuous monitoring, targeted interventions, and transparent reporting across diverse industry contexts. Full article
(This article belongs to the Special Issue Data-Driven Methods in Business Process Management)
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30 pages, 1020 KiB  
Article
Beyond the Counter: A Systemic Mapping of Nanostore Identities in Traditional, Informal Retail Through Multi-Dimensional Archetypes
by David Ernesto Salinas-Navarro, Eliseo Vilalta-Perdomo and Christopher Mejía-Argueta
Systems 2025, 13(7), 546; https://doi.org/10.3390/systems13070546 - 5 Jul 2025
Viewed by 245
Abstract
This study examines the identity of nanostores—micro, independent grocery retailers—through a systemic, stakeholder-informed lens to promote their survivability and competitiveness. Moving beyond traditional operational descriptions, it introduces a multidimensional framework that examines what nanostores do (X), how they do it (Y), and why [...] Read more.
This study examines the identity of nanostores—micro, independent grocery retailers—through a systemic, stakeholder-informed lens to promote their survivability and competitiveness. Moving beyond traditional operational descriptions, it introduces a multidimensional framework that examines what nanostores do (X), how they do it (Y), and why they matter (Z), which is complemented by the use of the TASCOI tool to produce identity statements. Based on survey data collection and a thematic analysis of nanostore stakeholder responses in Mexico City, the research categorises identity statements into six 2 × 2 matrices across four dimensions: operational, functional, relational, and adaptive. This analysis yields twenty-four archetypes that capture the diversity, complexity, and adaptability of nanostores. The findings reveal that nanostores are not a homogeneous category. They simultaneously exhibit characteristics of multiple archetypes, blending retail function, social embeddedness, and entrepreneurial adaptation. This study contributes to the nanostore and micro-enterprise literature by operationalising identity description and offers practical insights for supporting diverse shop types through context-sensitive policy and business strategies. While this study ensures internal validity and reliability through systematic coding and stakeholder feedback, it acknowledges limitations in its generalisability. Future research may build on this work through comparative studies, longitudinal tracking, and direct engagement with nanostore owners and their communities to further understand the dynamics of their identity and their resilience in evolving retail landscapes. Full article
(This article belongs to the Special Issue Systems Thinking: Insights and Solutions to Complex Societal Challenges)
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27 pages, 3702 KiB  
Article
Domain Knowledge-Enhanced Process Mining for Anomaly Detection in Commercial Bank Business Processes
by Yanying Li, Zaiwen Ni and Binqing Xiao
Systems 2025, 13(7), 545; https://doi.org/10.3390/systems13070545 - 4 Jul 2025
Viewed by 115
Abstract
Process anomaly detection in financial services systems is crucial for operational compliance and risk management. However, traditional process mining techniques frequently neglect the detection of significant low-frequency abnormalities due to their dependence on frequency and the inadequate incorporation of domain-specific knowledge. Therefore, we [...] Read more.
Process anomaly detection in financial services systems is crucial for operational compliance and risk management. However, traditional process mining techniques frequently neglect the detection of significant low-frequency abnormalities due to their dependence on frequency and the inadequate incorporation of domain-specific knowledge. Therefore, we develop an enhanced process mining algorithm by incorporating a domain-specific follow-relationship matrix derived from standard operating procedures (SOPs). We empirically evaluated the effectiveness of the proposed algorithm based on real-world event logs from a corporate account-opening process conducted from January to December 2022 in a Chinese commercial bank. Additionally, we employed large language models (LLMs) for root cause analysis and process optimization recommendations. The empirical results demonstrate that the E-Heuristic Miner significantly outperforms traditional machine learning methods and process mining algorithms in process anomaly detection. Furthermore, the integration of LLMs provides promising capabilities in semantic reasoning and offers explainable optimization suggestions, enhancing decision-making support in complex financial scenarios. Our study significantly improves the precision of process anomaly detection in financial contexts by incorporating banking-specific domain knowledge into process mining algorithms. Meanwhile, it extends theoretical boundaries and the practical applicability of process mining in intelligent, semantic-aware financial service management. Full article
(This article belongs to the Special Issue Business Process Management Based on Big Data Analytics)
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14 pages, 931 KiB  
Article
Using Systems Thinking to Manage Tourist-Based Nutrient Pollution in Belizean Cayes
by Daniel A. Delgado, Martha M. McAlister, W. Alex Webb, Christine Prouty, Sarina J. Ergas and Maya A. Trotz
Systems 2025, 13(7), 544; https://doi.org/10.3390/systems13070544 - 4 Jul 2025
Viewed by 87
Abstract
Tourism offers many economic benefits but can have long-lasting ecological effects when improperly managed. Tourism can cause overwhelming pressure on wastewater treatment systems, as in Belize, where some of the over 400 small islands (cayes) that were once temporary sites for fishermen have [...] Read more.
Tourism offers many economic benefits but can have long-lasting ecological effects when improperly managed. Tourism can cause overwhelming pressure on wastewater treatment systems, as in Belize, where some of the over 400 small islands (cayes) that were once temporary sites for fishermen have become popular tourist destinations. An overabundance of nitrogen, in part as a result of incomplete wastewater treatment, threatens human health and ecosystem services. The tourism industry is a complex and dynamic industry with many sectors and stakeholders with conflicting goals. In this study, a systems thinking approach was adopted to study the dynamic interactions between stakeholders and the environment at Laughing Bird Caye National Park in Belize. The project centered on nutrient discharges from the caye’s onsite wastewater treatment system. An archetype analysis approach was applied to frame potential solutions to nutrient pollution and understand potential behaviors over time. “Out of control” and “Underachievement” were identified as system archetypes; “Shifting the Burden” and ‘‘Limits to Success’’ were used to model specific cases. Based on these results, upgrading of the wastewater treatment system should be performed concurrently with investments in the user experience of the toilets, education on the vulnerability of the treatment system and ecosystem, and controls on the number of daily tourists. Full article
(This article belongs to the Special Issue Systems Thinking: Insights and Solutions to Complex Societal Challenges)
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