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Systems
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Decision-Making in Sustainable Business Models: Prediction and Modeling
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Decision-Making in Sustainable Business Models: Prediction and Modeling

A special issue of Systems (ISSN 2079-8954). This special issue belongs to the section "Systems Theory and Methodology".

Deadline for manuscript submissions: closed (30 April 2026) | Viewed by 25398

Special Issue Editors

Dr. Martin Holubčík

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Guest Editor
Faculty of Management Science and Informatics, University of Žilina, 010 26 Žilina, Slovakia
Interests: management decision-making; planning and controlling; cooperation management; strategy management; marketing; digital marketing; transport and logistics
Prof. Dr. Jakub Soviar

E-Mail Website
Guest Editor
Faculty of Management Science and Informatics, University of Žilina, 010 26 Žilina, Slovakia
Interests: management and marketing; sociological research; sustainable business; sustainable cooperation and cooperation management; marketing strategy; online reputation management
Special Issues, Collections and Topics in MDPI journals
Dr. František Pollák

E-Mail Website
Guest Editor
Department of Tourism and Marketing, Faculty of Corporate Strategy, Institute of Technology and Business, Okruzni 517/10, 37001 Ceske Budejovice, Czech Republic
Interests: management; international business; business consulting; project management; innovation; marketing management; leadership; business development; business; entrepreneurship
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue of Systems is focused on decision-making in sustainable business models. This research area is focused on today’s problems in sustainable business, which can bring new ideas into the future.

Sustainable business models are dynamic. In a social system, their actors and their relationships are constantly evolving and changing. Since a business model can be understood as a system, its sustainability must be constantly monitored, adapted according to current conditions, and, ideally, improved. Correct management decisions are, therefore, critically important; they fundamentally affect the functioning of these systems. Modeling or creating simulations is very beneficial for a proper understanding of the system functioning of business models.

Decision-making at the top corporate level is of strategic importance; its result is the real sustainability and competitiveness of the given business. The problem with these management decisions is precisely the lack of data and the lack of simulations, modeling, research, and analysis. Correct decisions, which are based on an analytical approach, will make it possible to achieve a sustainable business model and thus create a functioning sustainable system.

Dr. Martin Holubčík
Prof. Dr. Jakub Soviar
Dr. František Pollák
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Systems is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • sustainable business models
  • business management
  • sustainable business systems
  • sustainable
  • business models
  • decision-making in business
  • management decision-making
  • prediction in business
  • modeling in business

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Published Papers (8 papers)

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22 pages, 654 KB  
Article
Strategic Smart City Development Through Citizen Participation: Empirical Evidence from Slovakia
by Oliver Bubeliny, Dana Kusnirova and Milan Kubina
Systems 2026, 14(1), 43; https://doi.org/10.3390/systems14010043 - 30 Dec 2025
Viewed by 664
Abstract
The article examines the relationship between city size, strategic capacity, and stakeholder participation in the development of Smart City strategies in Slovak municipalities. Although Smart City initiatives in Central and Eastern Europe are expanding, empirical evidence on their strategic foundations remains limited. This [...] Read more.
The article examines the relationship between city size, strategic capacity, and stakeholder participation in the development of Smart City strategies in Slovak municipalities. Although Smart City initiatives in Central and Eastern Europe are expanding, empirical evidence on their strategic foundations remains limited. This research bridges that gap by analyzing data collected from 35 Slovak cities with populations above 20,000 inhabitants. Using a structured questionnaire and applying nonparametric statistical methods (Spearman correlation, Chi-square test, ANOVA, and Kruskal–Wallis test), the study explores how city size affects the presence and thematic orientation of Smart City strategies as well as the intensity of stakeholder participation. The results reveal a moderate and statistically significant correlation between city size and Smart City strategy development, while thematic orientations remain similar across cities. Larger municipalities show higher levels of strategic capacity and greater cooperation with academic institutions, confirming partial support for the proposed hypotheses. The findings underscore the need to strengthen the institutional and participatory capacities of smaller municipalities to achieve balanced and inclusive Smart City governance. The study contributes to the literature by integrating strategic, technological, and participatory dimensions into one analytical framework applicable to the CEE context. Full article
(This article belongs to the Special Issue Decision-Making in Sustainable Business Models: Prediction and Modeling)
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20 pages, 609 KB  
Article
Prescriptive Analytics for Sustainable Financial Systems: A Causal–Machine Learning Framework for Credit Risk Management and Targeted Marketing
by Jaeyung Huh
Systems 2026, 14(1), 16; https://doi.org/10.3390/systems14010016 - 24 Dec 2025
Cited by 1 | Viewed by 1537
Abstract
Financial institutions increasingly rely on data-driven decision systems; however, many operational models remain purely predictive, failing to account for confounding biases inherent in observational data. In credit settings characterized by selective treatment assignment, this limitation can lead to erroneous policy assessments and the [...] Read more.
Financial institutions increasingly rely on data-driven decision systems; however, many operational models remain purely predictive, failing to account for confounding biases inherent in observational data. In credit settings characterized by selective treatment assignment, this limitation can lead to erroneous policy assessments and the accumulation of “methodological debt”. To address this issue, we propose an “Estimate → Predict & Evaluate” framework that integrates Double Machine Learning (DML) with practical MLOps strategies. The framework first employs DML to mitigate selection bias and estimate unbiased Conditional Average Treatment Effects (CATEs), which are then distilled into a lightweight Target Model for real-time decision-making. This architecture further supports Off-Policy Evaluation (OPE), creating a “Causal Sandbox” for simulating alternative policies without risky experimentation. We validated the framework using two real-world datasets: a low-confounding marketing dataset and a high-confounding credit risk dataset. While uplift-based segmentation successfully identified responsive customers in the marketing context, our DML-based approach proved indispensable in high-risk credit environments. It explicitly identified “Sleeping Dogs”—customers for whom intervention paradoxically increased delinquency risk—whereas conventional heuristic models failed to detect these adverse dynamics. The distilled model demonstrated superior stability and provided consistent inputs for OPE. These findings suggest that the proposed framework offers a systematic pathway for integrating causal inference into financial decision-making, supporting transparent, evidence-based, and sustainable policy design. Full article
(This article belongs to the Special Issue Decision-Making in Sustainable Business Models: Prediction and Modeling)
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24 pages, 587 KB  
Article
Maximizing Shareholder Wealth Through Strategic M&A: The Impact of Target Firm Listing Status and Acquirer Size on Sustainable Business Models in Korean SMEs
by Sung-woo Cho and Jin-young Jung
Systems 2025, 13(10), 896; https://doi.org/10.3390/systems13100896 - 10 Oct 2025
Viewed by 1465
Abstract
Strategic mergers and acquisitions (M&A) can support sustainable business models by enabling firms to adapt their capabilities and competitive positions as conditions change. This study examines how target listing status (public vs. private) and acquirer size shape short-term shareholder wealth in Korean SMEs [...] Read more.
Strategic mergers and acquisitions (M&A) can support sustainable business models by enabling firms to adapt their capabilities and competitive positions as conditions change. This study examines how target listing status (public vs. private) and acquirer size shape short-term shareholder wealth in Korean SMEs (Small- and medium-sized enterprise), and links announcement reactions to subsequent operating outcomes. Using an event study and multivariate regressions on 155 M&A announcements by KOSDAQ-listed SMEs (Korean Securities Dealers Automated Quotations) (2016–2020), we find that smaller acquirers earn significantly higher announcement-period cumulative abnormal returns (CAR)—i.e., smaller firm size is positively associated with superior market-adjusted performance around M&A events. Although acquisitions of privately held targets and diversifying deals show higher unadjusted means, their effects become statistically insignificant once firm fundamentals and size are controlled for. To connect M&A strategy with business-model sustainability, we operationalize sustainability as the alignment between short-term market expectations (CAR) and realized operating performance over 1–2 years, measured by return on operating cash flow (ROCF); medium-term checks indicate that the short-run “size effect” attenuates, underscoring the role of execution and scale in longer-run outcomes. Overall, the evidence highlights the primacy of firm-specific fundamentals, strategic fit, and integration capacity in guiding M&A decisions that advance both near-term performance and longer-term resilience. The Korean SME setting—marked by concentrated ownership, resource constraints, and a chaebol-influenced market and policy environment—provides a stringent context for these tests. Full article
(This article belongs to the Special Issue Decision-Making in Sustainable Business Models: Prediction and Modeling)
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32 pages, 2590 KB  
Article
Model for Innovation Project Selection Supported by Multi-Criteria Methods Considering Sustainability Parameters
by Jamile Eleutério Delesposte, Luís Alberto Duncan Rangel, Marcelo Jasmim Meiriño, Carlos Manuel dos Santos Ferreira, Rui Jorge Ferreira Soares Borges Lopes and Ramon Baptista Narcizo
Systems 2025, 13(10), 876; https://doi.org/10.3390/systems13100876 - 7 Oct 2025
Cited by 1 | Viewed by 1580
Abstract
Innovation projects with sustainable characteristics are increasingly seen as strategic drivers for organizations to expand market share and retain customers. Yet, firms face limited resources while dealing with many potential projects. To address this challenge, an integrated framework for evaluating and ranking innovation [...] Read more.
Innovation projects with sustainable characteristics are increasingly seen as strategic drivers for organizations to expand market share and retain customers. Yet, firms face limited resources while dealing with many potential projects. To address this challenge, an integrated framework for evaluating and ranking innovation projects using sustainability-related factors can support more consistent decision-making. Although several models for project selection exist in the literature, few provide a comprehensive approach that incorporates sustainability criteria. This study proposes a model for selecting innovation projects by explicitly considering sustainability aspects, supported by multi-criteria decision support methods. The methodological approach followed the Design Cycle method, grounded in Design Science Research. The main result is a novel, customizable model for evaluating, ranking, and managing innovation projects within a sustainability-oriented context. The model was validated through application in two high-performance organizations recognized for their innovation and sustainability practices. Additionally, this research offered reflections on how sustainability-driven innovation can be implemented in practice. Overall, the findings demonstrated that the proposed model is adaptable to different organizational realities, sectors, and sizes, enhancing the capacity to assess and understand the role of sustainability in innovation projects more effectively. Full article
(This article belongs to the Special Issue Decision-Making in Sustainable Business Models: Prediction and Modeling)
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32 pages, 1886 KB  
Article
A PDCA-Based Decision-Making Framework for Sustainable Marketing Communication Strategies: A Case Study of a Slovak Telecommunications Company
by Miroslava Řepová, Lucie Lendelová and Viliam Lendel
Systems 2025, 13(8), 721; https://doi.org/10.3390/systems13080721 - 21 Aug 2025
Viewed by 3330
Abstract
With the rapid development of technology, an increasingly competitive environment, and evolving consumer behaviour, the use of modern marketing tools has become a key challenge for companies of various types (manufacturing, providing services, sports organizations, universities, etc.). Although sustainable digital communication methods are [...] Read more.
With the rapid development of technology, an increasingly competitive environment, and evolving consumer behaviour, the use of modern marketing tools has become a key challenge for companies of various types (manufacturing, providing services, sports organizations, universities, etc.). Although sustainable digital communication methods are gaining prominence, existing research often focuses merely on describing communication trends without providing decision-making frameworks for strategy optimisation. This paper addresses this gap by mapping the current state of marketing communication strategies among large telecommunication companies in Slovakia and assessing their impact on customer behaviour and market position. Data were analysed through a combination of qualitative and quantitative research methods, including document analysis, annual reports, surveys, and personal observations. One enterprise was selected for detailed data analysis. The results confirm a significant relationship between the use of communication channels and the company’s market position, brand popularity, and the strong influence of employee recommendations. Unlike previous studies, which predominantly describe marketing communication trends and tools, this research integrates the evaluation of communication strategy effectiveness with a systematic management decision-making model based on the PDCA (Plan-Do-Check-Act) continuous improvement cycle. This approach enables continuous optimisation of sustainable communication strategies and provides actionable managerial guidance for improving resource allocation, market position, and organisational adaptability in dynamic market environments. Full article
(This article belongs to the Special Issue Decision-Making in Sustainable Business Models: Prediction and Modeling)
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24 pages, 4383 KB  
Article
Predicting Employee Attrition: XAI-Powered Models for Managerial Decision-Making
by İrem Tanyıldızı Baydili and Burak Tasci
Systems 2025, 13(7), 583; https://doi.org/10.3390/systems13070583 - 15 Jul 2025
Cited by 18 | Viewed by 8212
Abstract
Background: Employee turnover poses a multi-faceted challenge to organizations by undermining productivity, morale, and financial stability while rendering recruitment, onboarding, and training investments wasteful. Traditional machine learning approaches often struggle with class imbalance and lack transparency, limiting actionable insights. This study introduces an [...] Read more.
Background: Employee turnover poses a multi-faceted challenge to organizations by undermining productivity, morale, and financial stability while rendering recruitment, onboarding, and training investments wasteful. Traditional machine learning approaches often struggle with class imbalance and lack transparency, limiting actionable insights. This study introduces an Explainable AI (XAI) framework to achieve both high predictive accuracy and interpretability in turnover forecasting. Methods: Two publicly available HR datasets (IBM HR Analytics, Kaggle HR Analytics) were preprocessed with label encoding and MinMax scaling. Class imbalance was addressed via GAN-based synthetic data generation. A three-layer Transformer encoder performed binary classification, and SHapley Additive exPlanations (SHAP) analysis provided both global and local feature attributions. Model performance was evaluated using accuracy, precision, recall, F1 score, and ROC AUC metrics. Results: On the IBM dataset, the Generative Adversarial Network (GAN) Transformer model achieved 92.00% accuracy, 96.67% precision, 87.00% recall, 91.58% F1, and 96.32% ROC AUC. On the Kaggle dataset, it reached 96.95% accuracy, 97.28% precision, 96.60% recall, 96.94% F1, and 99.15% ROC AUC, substantially outperforming classical resampling methods (ROS, SMOTE, ADASYN) and recent literature benchmarks. SHAP explanations highlighted JobSatisfaction, Age, and YearsWithCurrManager as top predictors in IBM and number project, satisfaction level, and time spend company in Kaggle. Conclusion: The proposed GAN Transformer SHAP pipeline delivers state-of-the-art turnover prediction while furnishing transparent, actionable insights for HR decision-makers. Future work should validate generalizability across diverse industries and develop lightweight, real-time implementations. Full article
(This article belongs to the Special Issue Decision-Making in Sustainable Business Models: Prediction and Modeling)
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29 pages, 1282 KB  
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
Cited by 7 | Viewed by 4535
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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26 pages, 461 KB  
Systematic Review
Artificial Intelligence in Managerial Decision-Making for Sustainable Business Models: A Systematic Literature Review
by Michal Urbanovič and Martin Holubčík
Systems 2026, 14(3), 245; https://doi.org/10.3390/systems14030245 - 27 Feb 2026
Viewed by 2361
Abstract
Managerial decision-making is a core component of business management and plays a particularly critical role in Sustainable Business Models (SBMs), where it supports long-term competitiveness, adaptability, and positive environmental and social impact. SBMs are inherently complex, dynamic, and data-intensive, requiring advanced analytical capabilities [...] Read more.
Managerial decision-making is a core component of business management and plays a particularly critical role in Sustainable Business Models (SBMs), where it supports long-term competitiveness, adaptability, and positive environmental and social impact. SBMs are inherently complex, dynamic, and data-intensive, requiring advanced analytical capabilities to continuously monitor and optimize sustainability performance across Environmental, Social, and Governance (ESG) dimensions. Artificial Intelligence (AI) introduces new technological opportunities that fundamentally transform managerial decision-making by enabling advanced modeling, simulation, and the analysis of incomplete and heterogeneous data. The purpose of this research is to systematically analyze and synthesize existing AI-supported decision-making approaches used in sustainable business models, with a focus on how these methods transform traditional managerial decision-making frameworks through the integration of Environmental, Social, and Governance (ESG) criteria, and to assess the key benefits, limitations, and implementation conditions of AI-supported decision systems for achieving long-term organizational sustainability. Using a systematic literature review and comparative synthesis of recent theoretical and empirical studies, the research maps key AI-based decision-making approaches applied in sustainable business models and compares their managerial relevance across ESG dimensions. The results provide a structured overview of how different AI techniques contribute to sustainability monitoring, resource optimization, and risk assessment, while also outlining critical organizational, governance, and ethical constraints affecting their practical deployment. Full article
(This article belongs to the Special Issue Decision-Making in Sustainable Business Models: Prediction and Modeling)
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