The integration of blockchain technology into supply chain management (SCM) has emerged as a revolutionary force transforming traditional business operations. This study uses bibliometric analysis on 1069 articles from the Scopus database, using text mining and Python to uncover predominant themes and research trends at the intersection of blockchain and SCM. The key findings revealed three main thematic groups: ‘blockchain to improve transparency and traceability in SCM’ (supported by 323 articles), ‘impact of blockchain on supply chain efficiency and cost reduction’ (295 articles), and ‘blockchain-enabled supply chain resilience’ (191 articles). Furthermore, text mining highlighted prominent themes such as ‘decentralized supply chain networks’ (204 articles), ‘smart contracts for automated processes in SCM’ (234 articles), and ‘blockchain for sustainable supply chain practices’ (227 articles). The inclusion of sustainability themes reflects the growing importance of environmentally conscious strategies within supply chains, driven by the capacity of blockchain to reduce waste, and promote resource efficiency. The study identifies critical literature gaps, advocating for further exploration of the socio-economic impacts of blockchain on SCM. The topic extraction suggests new directions for SCM theory, while the role of blockchain in fostering sustainable and ethical supply chains is underscored. Practically, blockchain and IoT emerge as pivotal in the advancement of SCM, with text mining offering industry foresight and emphasizing blockchain-driven resilient strategies. Limitations include reliance on a single database and the recommendation that future studies incorporate diverse sources and qualitative insights. The findings provide a roadmap for academics and practitioners, highlighting potential avenues in SCM, especially in the context of sustainable and ethical practices.

Artificial Intelligence (AI) is transforming sustainable business strategies globally, yet its specific applications within American enterprises remain underexplored. This study examines the integration of AI in sustainability efforts across various industries in the USA from 2014 to 2022. By analyzing 263 sustainability reports from 41 leading Nasdaq-listed firms using advanced text mining techniques, we uncover nuanced insights into how AI is employed to address environmental and social challenges. Our findings reveal a strategic deployment of AI not only to enhance operational efficiency, but also to drive significant environmental improvements, such as optimizing renewable energy usage and mitigating emissions. Additionally, AI’s impact extends to fostering workplace safety, enhancing diversity, and bolstering community initiatives. This research highlights the critical role of AI as a catalyst in advancing sustainable practices, providing a blueprint for other regions and industries aiming to leverage technology for greater sustainability.

This study investigates the effectiveness of machine learning and deep learning models for early prediction of student performance in higher education institutions. Using the Open University Learning Analytics (OULA) dataset, various models, including Decision Tree, Support Vector Machine, Neural Network, and Ensemble Model, were employed to predict student performance in three categories: Pass/Fail, Close to Fail, and Close to Pass. The Ensemble Model (EM) consistently outperformed other models, achieving the highest overall F1 measure, precision, recall, and accuracy. These results highlight the potential of data-driven techniques in informing educational stakeholders’ decision-making processes, enabling targeted interventions, and facilitating personalized learning strategies tailored to students’ needs. By identifying at-risk students early in the academic year, institutions can provide additional support to improve academic outcomes and retention rates. The study also discusses practical implications, including the development of pedagogical policies and guidelines based on early predictions, which can help educational institutions maintain strong academic outcomes and enhance their reputation for academic excellence. Future research aims to investigate the impact of individual activities on student performance and explore day-to-day student behaviors, enabling the creation of tailored pedagogical policies and guidelines.

Purpose: This study aims to deepen the understanding of consumer engagement and satisfaction within the health and wellness tourism sector, a rapidly growing niche in the global tourism industry. It focuses on identifying key elements that influence consumer perceptions and experiences in this domain. Design/methodology/approach: Employing a quantitative approach, this research utilizes Dynamic Correlated Topic Models (DCTM) and sentiment analysis techniques to analyze user-generated content from TripAdvisor. The methodology involves parsing through extensive online reviews to extract thematic patterns and emotional sentiments related to various wellness tourism experiences. Findings: The findings reveal that wellness and relaxation, spa and therapy services, and cultural immersion are significant factors influencing consumer satisfaction in health and wellness tourism. These elements contribute to a more profound and emotionally satisfying tourist experience, highlighting the shift from traditional tourism to more holistic, wellness-focused travel. Research limitations/implications: The study is limited by its focus on user-generated content from a single platform, which may not fully represent the diverse range of consumer experiences in health and wellness tourism. Future research could expand to include other platforms and cross-reference with qualitative data. Practical implications: The study offers valuable implications for destination managers and marketers in the health and wellness tourism industry, suggesting that enhancing and promoting wellness-centric experiences can significantly improve consumer satisfaction and engagement. Social implications: The research underscores the growing importance of health and wellness in societal values, reflecting a shift in consumer preferences towards travel experiences that offer mental, physical, and spiritual benefits. This has broader implications for how destinations can cater to the evolving demands of socially conscious travelers. Originality/value: This research contributes original insights into the evolving field of health and wellness tourism by integrating advanced text mining techniques to analyze consumer feedback, offering a novel perspective on what drives engagement and satisfaction in this sector.