This study proposes a predictive framework that integrates machine learning techniques with Transactional Net Promoter Score (tNPS) data to enhance sustainable Human Resource management. A synthetically generated dataset, simulating real-world employee feedback across divisions and departments, was used to classify employee performance and engagement levels. Six machine learning models such as XGBoost, TabNet, Random Forest, Support Vector Machines, K-Nearest Neighbors, and Neural Architecture Search were applied to predict high-performing and at-risk employees. XGBoost achieved the highest accuracy and robustness across key performance metrics, including precision, recall, and F1-score. The findings demonstrate the potential of combining real-time sentiment data with predictive analytics to support proactive HR strategies. By enabling early intervention, data-driven workforce planning, and continuous performance monitoring, the proposed framework contributes to long-term employee satisfaction, talent retention, and organizational resilience, aligning with sustainable development goals in human capital management.

The rapid rise of technologies like AI, IoT, and blockchain is transforming supply chain management by boosting efficiency, transparency, and resilience. This paper examines how these technologies optimize supply chains, focusing on predictive analytics, real-time monitoring, and secure data exchange. AI excels in automation and decision-making, IoT enables real-time data gathering, and blockchain ensures trust and transparency. Case studies from Amazon, Maersk, Walmart, and Pfizer illustrate improvements in efficiency, risk reduction, and cost savings. Additionally, challenges like high costs, data privacy, and lack of standardization are discussed, along with future trends in edge computing, quantum computing, and digital twins. The study highlights AI, IoT, and blockchain as essential to building smarter, adaptable supply chains capable of thriving in today's global economy.

This research evaluates the extent to which firms’ “green” bond disclosures create and convey a meaningful representation of their Environmental, Social, and Governance (“ESG”) commitments. Additionally, this research explores how investors distinguish between disclosures that represent genuine commitment to sustainability and those that may be indicative of “greenwashing,” and how such distinctions impact their assessment of an issuer’s credibility as well as the issuer’s performance subsequent to the issuance of a “green” bond. The methodology employed in this research employs a convergent mixed-methods approach that combines quantitative methods (Natural Language Processing (“NLP”), financial modeling, etc.) with qualitative methodologies (case studies, interviews). The NLP methodology employed in this research includes sentiment analysis, topic modeling, and ambiguity measurement in order to determine the tone, thematic content, and linguistic clarity of the disclosure texts. Subsequently, the results of the NLP methodologies are correlated with firm level outcomes using cross validated partial least squares regression (“PLS-R”), event study methodologies, and one way ANOVA to test for temporal and industrial variability. Finally, the results of the computational and financial methodologies are supplemented by qualitative case studies and interviews to provide context for the patterns identified in the computational and financial methodologies. In summary, the results of this research demonstrate that firms that communicate in a clear, balanced, and verifiable manner experience better market reaction and more favorable accounting results subsequent to the issuance of a “green” bond than do firms whose communications are vague, overly optimistic, or lacking in consistency. Conversely, the findings suggest that investors have become increasingly sensitive to potential “greenwashing” and therefore are less likely to respond favorably to communications characterized by the aforementioned characteristics.

This paper explores the transformative impact of cutting-edge technologies, specifically AI and blockchain, on economic well-being. It begins by examining how AI-driven solutions are revolutionizing industries, enhancing decision-making processes, and improving financial inclusion. Blockchain technology is discussed for its potential to increase transparency, reduce fraud, and streamline financial transactions, thereby fostering trust and efficiency. The synergy between AI and blockchain is highlighted as a powerful driver for economic growth, enabling decentralized finance, secure data management, and scalable innovation. The paper further explores how these technologies are unlocking new opportunities for sustainable development, particularly in emerging markets. It concludes by emphasizing the role of AI and blockchain in shaping a future of economic prosperity, with a focus on inclusivity, sustainability, and long-term well-being.

Software fault prediction (SFP) plays a critical role in improving software reliability by enabling early detection and correction of defects. This paper presents a comprehensive review of 25 recent and significant studies on SFP techniques, focusing on data preprocessing strategies, classification algorithms, and their effectiveness across various datasets. The review categorizes the approaches into traditional statistical models, machine learning methods, deep learning architectures, and hybrid techniques. Notably, wrapper-based feature selection, neural network classifiers, and support vector machines (SVM) are identified as the most effective in achieving high accuracy, particularly when dealing with imbalanced or noisy datasets. The paper also highlights advanced approaches such as variational autoencoders (VAE), Bayesian classifiers, and fuzzy clustering for fault prediction. Comparative analysis is provided to assess performance metrics such as accuracy, F-measure, and area under the curve (AUC). The findings suggest that no single method fits all scenarios, but a combination of appropriate preprocessing and robust classification yields optimal results. This review provides valuable insights for researchers and practitioners aiming to enhance software quality through predictive analytics. Future work should explore ensemble learning and real-time SFP systems for broader applicability.