The scientific literature related to business ecosystems is growing. However, the major part of this literature is devoted to so-called Digital Business Ecosystems (DBE) that are largely related to only the networking part of the business ecosystems, i.e., exchange of values among their participants. Other parts related to the work of a participating enterprise, as well as its suppliers and regulators, are often left outside. In this work, we take a different approach, namely, to model an ecosystem that exists around a specific enterprise. The work is based on the concepts transferred to business from biological cybernetics, such as structural coupling and autopoiesis. In this work, the Fractal Enterprise Model (FEM) is used for modeling business ecosystems. The goal is to suggest some patterns expressed in the modeling language that can help an enterprise build a model of the ecosystem of which the enterprise is in focus.
Artificial Intelligence (AI) has attracted significant attention among researchers and practitioners as it emerges as a strategic asset for organizations across sectors and industries. Within the public sector, the deployment of AI is anticipated to enhance the responsiveness of public organizations in delivering appropriate services and addressing complex societal challenges. This study examines the readiness of public organizations for AI adoption within Kenya’s public sector and explores its implications for public value creation. Anchored in the Technology-Organization-Environment framework and informed by Dynamic Capabilities theory, the article analyzes how structural conditions within organizations interact with adaptive capabilities to shape trajectories of AI readiness. Drawing on qualitative interviews with seventeen public sector experts, the study identifies a set and dynamic interdependence of critical readiness factors, including technological infrastructure, data quality, leadership commitment, staff competencies, organizational culture, regulatory frameworks, public trust, and external partnerships. By offering an empirically grounded and comparative perspective, the study aims to enhance our understanding of the relationship between AI readiness and public value creation, drawing on Kenya’s example. The results may also provide valuable inputs for policymakers in formulating actionable plans concerning differentiated implementation pathways, capacity development, and the ethical governance of AI in the public sector.
Complex Systems Informatics and Modeling Quarterly (CSIMQ) continues to serve as a forum for high-quality research at the intersection of informatics, modeling, and the study of complex socio-technical systems. This issue brings together seven contributions that reflect the breadth and depth of current research in these fields. The issue opens with two articles that explore advanced modeling and data-driven analysis within complex economic and organizational environments. The next two articles shift the focus toward the digital transformation of public services and the organizational capabilities needed to manage such change. The rest three articles address organizational transformation, IS integration challenges, and regulatory compliance in increasingly complex digital ecosystems. Together, the articles in this issue reflect the journal’s commitment to advancing knowledge on the modelling, governance, and transformation of complex systems. They showcase how researchers combine conceptual rigor, empirical investigation, and methodological innovation to address contemporary challenges in digital transformation, public-sector innovation, organizational alignment, and cybersecurity governance.
The revised Network and Information Security Directive (NIS2) aims to achieve a high level of common cybersecurity across the European Union. Several stakeholders, including member states, supervisory authorities, and critical infrastructure service providers, are expected to support this effort by ensuring a high level of information security. Part of increasing security levels involves implementing risk management measures by service providers, but also an evaluation of the security situation and its changes is necessary from the perspective of each stakeholder. Previous research has described NIS2-related activities through user stories that encompass six types of stakeholders with their respective goals in relation to the security level evaluation of organizations. In this article, we examine the real-life implementation of these security evaluation user stories and demonstrate how the framework for security level evaluation (F4SLE) can be utilized to achieve NIS2 compliance within this narrowed scope of security level evaluation. The advantage of F4SLE is that the data can be collected once and then reused to satisfy different stakeholders without imposing an additional reporting burden on entities that must be NIS2-compliant.
Post-merger integration (PMI) presents unique challenges for professionals responsible for information systems’ (IS) integration when aligning and combining diverse system architectures of merging organizations. Although the theoretical and practical guidance exists for PMI on the business level, there is a significant gap in training for IS integration in this context. In prior research, specific methods AMILI (Support method for informed decision identification) and AMILP (Support method for informed decision-making) were introduced for the support of IS integration decisions in PMI. But during the practical application, a high learning curve and low learner motivation were reported. This study examines how game-based learning can transform the training of these methods into a more engaging and motivating experience, tailored to the context of the specific PMI case. Building on instructional design theory, game design theory, cognitive load principles, motivation models, and specifics of the IS integration in the scope of the PMI, the article derives a structured catalogue of requirements for a game-based learning design framework tailored to PMI-specific conditions. Additionally, a metamodel is defined to show how these requirements are systematically transformed into design activities and artefacts, resulting in a consistent learning design process and a corresponding data model aimed at designing a learning experience for IS integration within the scope of a specific PMI. The metamodel’s applicability is demonstrated through instantiations of representative requirements.
Despite the increasing adoption of off-the-shelf ERP systems and the accumulation of industry experience, implementation failure rates remain high. One of the key causes is the persistent misalignment between standardized ERP processes and the specific processes of adopting organizations, which are commonly referred to as system-organizational gaps or misfits. While numerous studies acknowledge these misfits, only a few offer clear classifications of misfits and structured resolution frameworks. This article presents a systematic literature review of peer-reviewed academic studies from 2005 to 2025, aimed at identifying and synthesizing strategies for resolving ERP-related misfits. Given the methodological diversity of these sources, a mixed-method synthesis was employed: tabular analysis summarized key study features, while thematic synthesis uncovered recurring patterns in resolution strategies. The review identifies a wide variety of misfit types and corresponding resolution strategies. While conceptually rich, the literature lacks empirical comparisons of strategy effectiveness and remains fragmented across industries and phases of the ERP lifecycle. These gaps underline the need for future research to bridge theoretical insight with actionable, cross-contextual resolution models. The article concludes with recommendations for future research aimed at bridging the gap between conceptual understanding and practical application.
This article uses an experimental approach to examine the extent to which price fluctuations influence the profitability of electricity storages operating on the German Continuous Intraday Market. For this reason, we are extending our previous research and analyzing the trade-off between storage capacity and price forecast uncertainty with regard to arbitrage profitability. Using a genetic algorithm, we optimize buy and sell decisions for different battery storage sizes and simulated price forecast uncertainties over the year 2021. Our results show that a storage management strategy generated by the genetic algorithm enables significant arbitrage revenues, which rise in particular with increasing storage capacity. However, with an increasing battery size, decreasing marginal profits are to be expected. Price uncertainties due to forecast errors also reduce the gains, but their influence on profit remains moderate compared to the storage size. The genetic algorithm used provides an intelligent strategy for optimizing storage usage even under uncertain market conditions.
The success factors and challenges of e-government projects are often examined from the perspective of observers who analyze and document the current state of affairs. In most cases, however, no clearly designated entity is identified as responsible for fostering these success factors and mitigating the challenges. To make e-government success influencers more actionable, we propose a framework of active ownership of a public e-service that specifies the responsible entities along with their skills and tasks. The underlying rationale is that a public e-service is more usable and more widely adopted if it has an accountable and engaged owner. In addition to making e-government success factors actionable, the active ownership supports e-services through a “paradigm shift” in the management of e-government engagements: instead of fragmented managerial responsibilities distributed across time-restricted projects, a public e-service receives holistic, lifecycle-long management led by one accountable entity. Moreover, mapping the tasks related to active ownership to the components of transformational government reveals that these tasks align closely with the concepts of public digital transformation.
Object-centric process mining (OCPM) is an emerging research area that aims to analyze processes involving multiple object types (for instance, orders, items, and deliveries in an order-handling process) with complex intertwined relations captured in a richer format than traditional event logs. The richness of these data, as represented in the Object-Centric Event Log (OCEL) standard, often causes existing discovery algorithms to generate models overloaded with information, exceeding the cognitive limits of users, and reducing their practical usefulness. To address this challenge, we introduce Object-Centric Causal Nets (OCCN) together with an edge-abstraction technique that simplifies the discovered model by merging similar flows across object types. While OCCN provides native support for concurrency and choice, the edge abstraction is essential for reducing visual clutter and producing simpler yet expressive models. A Python implementation is provided, and a comparative evaluation against Object-Centric Petri Nets and Object-Centric Directly-Follows Graphs shows that OCCN with edge abstraction yields models that are easier to understand and more effective in enabling users to identify workflow patterns.
This issue of CSIMQ brings together a selection of five articles, each rigorously evaluated according to the following criteria: usefulness, scientific contribution, practical relevance, clarity, and technical quality. The articles included in this issue share a strong societal orientation, addressing challenges and innovations across diverse domains, such as healthcare, citizen services and protection, knowledge management, and sustainable development.
The exponential growth of digital information has exposed organizations to unprecedented challenges in managing and structuring their knowledge repositories. In the context of knowledge management, the ability to extract, organize, and use relevant information from large collections of documents has become a critical factor for operational efficiency and informed decision-making. However, identifying necessary knowledge sources and building appropriate knowledge bases represents a significant and time-consuming barrier. In this article, we address these challenges by leveraging advanced Natural Language Processing (NLP) techniques, particularly in combination with Large Language Models (LLMs), to facilitate the selection of more representative keywords for the creation and enrichment of vocabularies for knowledge management purposes. We explore the application of clustering techniques combined with NLP-driven keyword extraction to support the construction of specialized vocabularies that address the multidisciplinary nature of the content at CSTB, a French scientific research center focused on building science. We applied a pipeline with two approaches for keyword extraction: document-based clustering and chunk-based clustering. We provide a detailed overview of the proposed pipeline, present the results of our experiments, and describe the human validation process used to evaluate these results.
This study examines the mediating function of Green Innovation in the association between corporate social responsibility (CSR) initiatives and sustainable performance. It adopts a quantitative approach by collecting data from 129 recycling companies across various provinces in Indonesia through structured questionnaires distributed online. Partial Least Squares Structural Equation Modeling was conducted using the SmartPLS software to examine Green innovation’s function as a mediator between sustainable performance and CSR. The results indicate that green innovation has a pivotal mediating role between CSR initiatives and sustainable performance in Indonesia’s recycling sector. These results underscore the importance of long-term commitment and strategic alignment in overcoming short-term costs and complexities associated with implementing sustainability initiatives in an emerging industry. This study reveals the strategic role of companies in natural resource management as key to achieving sustainable performance amid the global climate crisis. It highlights the integration of environmental efforts, competitive capabilities, and sustainable innovation as a holistic approach rarely explored in the recycling industry of developing countries such as Indonesia. Positioning company capability as the main mediator, the study offers new insights into creating sustainable added value and strategic guidance for policymakers and practitioners. Additionally, it supports environmental, social, and governance and circular economy agendas, contributing significantly to the transition toward a green economy and sustainable development. In the domain of economics, this research can be attributed to the JEL Classification Code Q01; Q56; M14; L26; O31.
Accurate and early detection of Brain Tumors (BT) is pivotal to improve treatment planning and increase survival rates. A significant diagnostic system for the identification of brain disorders is Magnetic Resonance Imaging (MRI). In this study, a unique hybrid framework is developed by integrating InceptionV3, a deep learning model, with three machine learning models: AdaBoost, Random Forest (RF), and Logistic Regression (LR). High-dimensional spatial characteristics are extracted from pre-processed MRI data using the deep Inception model. Binary classification is then carried out by feeding these deep features into machine learning classifiers. Two hundred MRI images were used, half of which contained tumors and the other half of which did not. To ensure the reliability of the results, 50 distinct data splits and 10-fold cross-validation were employed. With an accuracy rate of 98.2% and an Area Under Curve (AUC) of 0.999, LR was the most successful. Next was RF, which had an accuracy of 94.6% and an AUC of 0.98. AdaBoost got an AUC of 0.874 and an accuracy of 87.4%. Experimental results prove that the hybrid technique achieves better classification accuracy and fewer false positives. The proposed framework is thus appropriate for clinical decision assistance since it strikes a compromise between learning depth and decision interpretability through the combination of deep feature representations and classifiers.
The deployment of surveillance networks in smart cities plays a pivotal role in enhancing public safety through the monitoring of various environments such as roads, airports, residential areas, and establishments. Nevertheless, the vast volumes of video data generated daily by these networks present both opportunities and challenges in terms of information management and analytical processing. In this study, we propose a novel trust-aware fusion framework of video-based violence and threat modeling by combining two state-of-the-art models. I3D, which excels in overall spatio-temporal reasoning, and C3D, which learns short-term motion behaviors. In Stackelberg’s game theory, the process of fusion outlines inference as a sequential decision-making process, wherein the leader is I3D, and C3D acts as a follower. A dynamic confidence threshold governs the prediction delegation power, enabling adaptive decision-making based on model confidence. Extensive experiments on a three-class dataset (Normal, Violence, Weaponized) prove that the introduced fusion strategy significantly outperforms single models. Setting the confidence threshold to 0.5 achieves 97.27% peak of overall accuracy. In addition, class-wise performance reveals considerable improvements, especially in the Violence class, where precision is 99% and the F1 score is 94%, versus 82% and 85% when using I3D individually. The experiments confirm the performance of the confidence-aware fusion for robust and context-adapted threat detection in smart-city surveillance.
The growth of IoT and connected devices has increased demand for low-latency, energy-efficient processing across the Cloud-Fog-Edge continuum. While microservices enable scalable distributed computing, their placement remains challenging due to dynamic resource needs and interdependencies. This work proposes a graph-based microservice placement approach using user-centered local community detection. By integrating user nodes, our method adapts to shifting demands and resource availability, reducing energy consumption and communication overhead. Additionally, strategic mutualization and controlled duplication further enhance efficiency while preserving response time and resource constraints. Our results highlight the effectiveness of user-centric strategies in achieving scalable and sustainable deployments, reducing energy consumption by approximately 50% compared to state-of-the-art global methods while slightly improving deployment time.
The rapid evolution of digital technologies continues to reshape complex systems across diverse domains. Business ecosystems, supply chains, consumer behavior, and environmental monitoring are domains where organizational and technological change have been constant over the years. This issue of Complex Systems Informatics and Modeling Quarterly (CSIMQ) presents five cutting-edge studies using data-driven approaches to address challenges in these areas. Through these contributions, the articles offer systematic reviews, advanced decision-making frameworks, psychological issues modeling, computational justice, and IoT innovations, advancing theoretical understanding and practical solutions for managing complexity in dynamic environments. Together, the articles contribute to the interdisciplinary mission of CSIMQ by bridging informatics, modeling, and systems thinking to drive innovation.
Ad hoc networks are self-organizing systems that operate without a centralized controller or orchestration mechanism. As a result, it is not possible to apply allocation methods designed for centralized systems, which typically require complete information and aim to optimize overall system performance without accounting for the individual interests of network members. To address this challenge, we propose a computational justice model for dynamic resource allocation, drawing on socially inspired computing and agent-based modeling. The model integrates stochastic games, the concept of social institutions, principles of distributive justice, and adaptive strategies to design an allocation mechanism guided by fairness and cooperation. A central contribution of this work is the conceptual integration of these components into a unified framework that supports dynamic resource allocation in decentralized environments. We evaluated our proposal through simulation and compared its performance with previous works. The results show that the proposed model ensures the endurance of available resources and maintains cooperative behavior among network members, even in the presence of selfish behaviors. These findings suggest that the proposed model is a potential solution for addressing dynamic allocation problems in ad hoc networks.
This study investigated the psychological mechanism through which Fear of Missing Out (FoMO) influenced consumers’ preferential hunting behavior on two leading e-commerce platforms in Vietnam: Shopee and Lazada. Drawing on an integrated theoretical framework combining FoMO theory, self-determination theory, scarcity theory, social influence theory, and perceived value theory, the research examined how FoMO affected deal-seeking intentions and behaviors through multiple pathways. A structural equation model was developed and tested using data collected from Vietnamese e-commerce users through an online survey. The analysis revealed that FoMO significantly influenced perceived scarcity, perceived benefits, and consumer sentiment, which subsequently shaped deal-seeking intention and actual preferential hunting behavior. The model demonstrated substantial explanatory power for both deal-seeking intention and preferential hunting behavior. Additionally, social influence and online shopping experience were confirmed as significant moderators, with social influence amplifying FoMO’s effects on perceptions and experience, strengthening the intention-behavior relationship. The findings advanced theoretical understanding of FoMO in commercial contexts and provided actionable insights for designing psychologically attuned promotional strategies in emerging e-commerce markets.
This study evaluates the role of Generative AI in optimizing digital supply chain performance, focusing on IoT integration, predictive analytics, and blockchain security. The primary objective is to determine which AI-driven initiatives offer the greatest benefits in enhancing resilience and operational efficiency. A structured multi-criteria decision-making approach is applied using the ELECTRE III method, leveraging quantitative data from DHL’s operational records (2022–2025). The evaluation is conducted with a panel of 18 industry experts, including logistics professionals and AI specialists, who participated in structured interviews and expert assessments to establish weighting criteria and performance metrics. Findings indicate that IoT-driven real-time tracking and predictive analytics for maintenance rank highest in enhancing supply chain resilience, improving operational responsiveness, and reducing downtime. Additionally, blockchain-supported security mechanisms reinforce data integrity and transparency, strengthening logistics security. Conversely, OCR-based automation and NLP-powered logistics systems demonstrate comparatively lower impact, emphasizing the need for targeted AI adoption strategies. This study contributes to structured AI evaluation methodologies by establishing a repeatable decision-making framework, ensuring scalability beyond DHL’s logistics operations. Limitations include the reliance on industry-specific datasets, which require further validation across diverse supply chain environments.
In business fields, an ecosystem, which is a shared environment consisting of organizations, individuals, resources, and technologies that are dynamically interconnected, is needed to provide and deliver emerging innovations and sustain competitive advantages. This article provides a thorough, systematic, and structured review of several frameworks of business ecosystems and their functioning. It also covers how data analytics can enhance ecosystem profiling by integrating it into these frameworks. Additionally, by considering advanced methodologies, this article highlights how actionable insights can be gained in the different operations and applications, from market analysis to risk management. Also, this review showcases several visualization tools that can be used to view complicated ecosystem structures, which helps employers to explore, analyze, and visualize data effectively. Finally, the article discusses the strengths and weaknesses of these tools and also offers some insights that can enhance decision-making in the emerging trends in the business ecosystem.
