International Journal of Management Science and Operations Research

A Bibliometric and Conceptual Framework for Ethical AI Integration in Sustainable Digital Leadership: Global Evidence and Emerging Trends

Chaofeng LYU — Fri, 30 Jan 2026 00:00:00 +0000

The rapid proliferation of artificial intelligence (AI) across organizational ecosystems has intensified the need for ethical governance frameworks that align technological innovation with sustainable leadership practices. This study conducts a systematic bibliometric and conceptual analysis of global scientific production on ethical AI integration in digital leadership. Using a dataset of 100 Web of Science-indexed publications (2020–2026), the research applies co-occurrence analysis, thematic clustering, and conceptual synthesis through tools such as VOSviewer and Bibliometrix.

The results identify four dominant research clusters: Ethical AI Systems, Digital Leadership, Sustainability Integration, and Governance & Regulation. Despite increasing interdisciplinary convergence, the literature remains structurally fragmented, lacking an integrated framework that connects these dimensions. To address this gap, the study proposes a conceptual model that establishes causal relationships between ethical AI, governance mechanisms, leadership decision-making, and sustainability outcomes within a feedback-driven system.

The contributions are threefold: theoretically, by integrating previously disconnected domains; methodologically, by combining bibliometric rigor with conceptual synthesis; and practically, by offering guidance for policymakers and organizational leaders. The findings support the development of responsible AI-driven strategies and provide a foundation for future research on sustainable digital transformation.

Comparative Machine Learning Models for Climate Risk Governance: Evidence from Global Environmental Datasets

Luis Avila Lopez — Sun, 15 Feb 2026 00:00:00 +0000

Climate change has significantly increased the complexity and uncertainty associated with environmental risk governance, requiring advanced predictive models capable of supporting decision-making processes under dynamic and nonlinear conditions. This study develops a comparative analytical framework to evaluate the performance of three machine learning models—Random Forest (RF), XGBoost, and Long Short-Term Memory (LSTM)—for climate risk prediction using global environmental datasets.

The research adopts a quantitative model-based design, integrating multi-variable data including temperature, CO₂ emissions, precipitation levels, and extreme weather events over the period 2000–2025. Model performance is assessed through k-fold cross-validation and metrics such as RMSE, MAE, and predictive accuracy.

The results indicate that XGBoost achieves the highest predictive performance in structured datasets, while LSTM demonstrates superior capacity for temporal pattern recognition. Despite these advances, the analysis reveals a structural limitation: current predictive models are rarely integrated into governance frameworks.

This study contributes by: (1) providing a comparative evaluation of machine learning models in climate risk prediction, (2) proposing an integrated analytical approach linking AI and governance, and (3) offering a replicable methodological framework for data-driven environmental decision systems.

Digital Twin–Driven Optimization in Smart Manufacturing: A Simulation-Based and Data-Driven Approach for Industrial Systems

Muhammad Moazzam Jawaid — Sun, 01 Mar 2026 00:00:00 +0000

The advancement of Industry 4.0 has accelerated the adoption of digital twin technologies; however, existing research remains fragmented, lacking integrated frameworks that combine data-driven analytics, simulation, and optimization. This study develops and validates a hybrid model that integrates digital twin architectures with simulation-based modeling and multi-objective optimization to enhance industrial system performance. A synthetic dataset of 1,000 observations was generated to represent industrial IoT environments, incorporating production, energy, operational, and sensor variables. The model was implemented using AnyLogic, Python, and MATLAB, and evaluated across different digital twin maturity scenarios. Results show statistically significant improvements in throughput (up to 17.8%), energy efficiency (up to 14.6%), and cost reduction (up to 18.3%), with strong model fit (R² = 0.72, p < 0.001). These findings confirm the effectiveness of integrating digital twins with simulation and optimization as a unified decision-making framework. The study contributes a scalable and empirically validated model that advances the integration of IoT, simulation, and optimization in smart manufacturing systems, offering both theoretical and practical implications for Industry 4.0

Algorithmic Bias and Justice in AI Systems: A Data-Driven NLP Analysis of Decision-Making Frameworks in Global Governance

Jhennys Becerra Ossa, Leidy Perez Coronell — Sun, 15 Mar 2026 00:00:00 +0000

The integration of artificial intelligence (AI) into decision-making systems has intensified concerns about algorithmic bias and its implications for fairness in global governance. Despite growing interest in AI ethics, empirical evidence based on real-world legal data remains limited. This study develops a data-driven framework to analyze bias in AI-assisted legal and administrative decisions using Natural Language Processing (NLP). A dataset of 420 legal cases across five continents (2018–2025) was examined using lexical and semantic bias indicators, fairness scores, and governance variables such as transparency and accountability. The methodology combines computational text analysis (SpaCy, NLTK) with statistical modeling, including correlation and regression, supported by expert validation. Results reveal a significant inverse relationship between bias and fairness, as well as a moderating effect of transparency, which reduces the impact of bias. AI involvement is found to amplify existing structural biases under low-transparency conditions. The findings demonstrate that algorithmic bias is a governance-dependent phenomenon and provide a scalable framework for improving fairness in AI-driven decision systems.

Cross-Border Innovation Networks and Strategic Alliances for Sustainability: A Panel Data Econometric Analysis of Global Partnerships

Natalia González Auque — Sat, 30 May 2026 00:00:00 +0000

The growing interdependence of global economies has reinforced the importance of cross-border innovation networks and strategic alliances as drivers of knowledge diffusion and sustainable development. Despite advances in innovation systems theory, empirical evidence linking international collaboration to sustainability outcomes remains limited. This study addresses this gap using a panel data econometric approach based on a balanced dataset of 60 countries over the period 2020–2024.

The model incorporates fixed and random effects estimations, validated through the Hausman test, and includes interaction terms to capture structural heterogeneity across productivity levels. Results show that cross-border alliances significantly enhance sustainability performance, with stronger effects in high-productivity economies. Innovation output acts as a partial mediator, while digitalization amplifies these effects.

The study contributes by integrating network theory with econometric modeling, providing robust evidence on the conditional impact of global innovation networks on sustainability.

AI-Driven Decision Support Systems for Strategic Planning: A Multi-Criteria Optimization Framework Integrating AHP, TOPSIS, and Machine Learning

Uzair Aslam Bhatti — Thu, 30 Apr 2026 00:00:00 +0000

This study develops a hybrid decision support system (DSS) that integrates artificial intelligence with multi-criteria decision-making techniques to enhance strategic planning in complex and uncertain environments. The research addresses key limitations of traditional DSS models, particularly their reliance on static criteria weighting and lack of adaptive capabilities.
The proposed framework combines the Analytic Hierarchy Process (AHP), the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), and machine learning to enable structured and adaptive decision-making. A synthetic dataset of 120 strategic scenarios was constructed to simulate realistic organizational conditions. The methodology includes consistency validation (AHP), multi-criteria ranking (TOPSIS), and predictive optimization using supervised learning models implemented in Python. Results show that the integrated model significantly improves decision quality, consistency, and adaptability, outperforming traditional approaches. Machine learning enhances the robustness of evaluation processes and enables dynamic adjustment of decision outcomes. This study contributes to the advancement of intelligent decision systems by bridging operations research and artificial intelligence, providing a scalable and reproducible framework for strategic decision-making.