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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: 31 July 2025 | Viewed by 942

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

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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 100 words) can be sent to the Editorial Office for announcement on this website.

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 (2 papers)

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Research

24 pages, 4383 KiB  
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
Viewed by 76
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 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
Viewed by 233
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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