The purpose of this study is to review global research trends in digital marketing as it refers to micro-entrepreneurs in a bibliometric methodology. An extensive search of open-access publications found in the Dimensions AI database during the period 20242025 with Leading authors, institutions, countries, and thematic clusters were visualized using VOSviewer performance, co-citation, and co-occurrence analysis. The results indicate that Asia, especially China, India, Malaysia, and Indonesia have emerged as a major source of this body of literature. The three key areas of research were identified as global digital strategies, new social media analytics, and influencer marketing to engage consumers. Despite these developments, gaps in the research on micro-entrepreneurs in developing nations and in qualitative research perspectives remain. Overall, The study draws different articles into rational subject groups and is accompanied by a conclusion that the body of existing research offers both theoretical integration and practical insight to entrepreneurs and policymakers willing to use digital marketing to promote their small businesses growth in the long term.

This study aims to analyze how the application of Scrum improves project development in companies and positively impacts the organizational environment. The research was conducted through a review of scientific articles published between 2013 and 2024, allowing the identification of the main motivators, challenges, and benefits of the agile methodology in organizations. The results show that Scrum improves internal communication, increases delivery efficiency, and enhances the quality of products and processes, delivering value to stakeholders. Furthermore, the analysis revealed that implementing the methodology depends on factors such as organizational culture, team training, and the team's understanding of agility. As a limitation, the study identified temporal variation in the articles: their distribution over the years was not homogeneous, and there was no in-depth quantitative evaluation to accurately measure the impacts of applying Scrum. Based on these conclusions, it is suggested that future research explores the integration of Scrum with other agile methodologies, conduct a comparative analysis with traditional methods, examine its application in specific sectors, and assess its impact on organizational culture.

Artificial Intelligence for sustainable education and green technologies has been immediately increasing awareness in the scholarly community, most of the studies address lone threads such as adaptive learning, smart energy management and sustainability without a contracted bibliometric integration. In this study, PRISMA-conducted bibliometric and network analysis exhaustively evaluated 1,699 Scopus-indexed papers (2020–2025) covering AI, sustainable education, and green technology to close this gap. Key findings include bibliographic coupling, co-citation and keyword co-occurrence patterns that emphasize sustainable development, green innovation and AI-enabled pedagogy, as well as strong annual growth (30.83%), prominent outlets (e.g., Sustainability), highly productive institutions (e.g., Bucharest University of Economic Studies), and coordination structures across authors and nations. The analysis maps widely researched fields and emerging themes, emphasizing worldwide North-South partnerships asymmetries, and articulates a roadmap for future work combining AI-driven educational revolution with low-carbon, resource-efficient technologies aligned to SDG 4 and SDG 13. This synthesis presents extensive evidence to assist policy and practice toward inclusive, data driven and environmentally responsible education systems.

Global supply chains are navigating ever more volatile and rapidly evolving conditions driven by technological innovation, geopolitical risks, environmental challenges, and changing customer requirements. Hence, conventional methods that focus on efficiency and cost reduction along the supply chain are not enough to stay competitive. In this context, an important strategic capability has emerged – the ability to be flexible, innovative, and adaptable, as well as efficient, standardised, and cost effective: supply chain ambidexterity. Notably, the extant literature on the relationship between SC ambidexterity and organizational performance have been scattered and segmented in various theoretical and operational viewpoints. To sum up previous research published between 2015 and 2025, this study used Systematic Literature Review (SLR) based on the PRISMA 2020 framework. A total of 34 peer-reviewed journal articles were included after screening and eligibility process both from Scopus and Web of Science database. The results reveal that supply chain ambidexterity has matured from a trade-off between exploration and exploitation, to a broader dynamic capability that is complemented by digital integration, resilience, analytics capability and organizational learning. Five key themes were found: conceptual foundations, lean–agile integration, digital technology enablement, performance outcomes, and environmental uncertainty. Organizations that are able to effectively blend efficiency with flexibility appear to have experienced an overall positive impact on their operational performance, innovation, responsiveness, resilience, and competitive advantage, according to the studies. Furthermore, some research gaps have been identified during the review, and future research recommendations are provided that could contribute to further research on the link between SC ambidexterity and OR performance.

This bibliometric survey examines 200 highly cited publications spanning from 2009 to 2025, retrieved from the Scopus database using the search strategy targeting neural technology and human augmentation. The analysis reveals a transformative evolution from foundational neuroscience discoveries to sophisticated artificial intelligence-driven augmentation systems. Key findings indicate that deep learning architectures, particularly convolutional neural networks (CNNs), generative adversarial networks (GANs), and transfer learning approaches have become dominant methodological frameworks across 78% of analyzed publications. Data augmentation emerges as a critical enabling technology, appearing in over 35% of publications, addressing the persistent challenge of limited labeled data in biomedical applications. The United States, China, Germany, India, and South Korea represent the most productive nations, with intensifying international collaboration networks from 2015 onward. Thematic clustering identifies five major research domains: (1) brain-computer interfaces and neural signal decoding, (2) deep learning for medical image analysis, (3) sensory augmentation and assistive technologies, (4) data augmentation and synthetic data generation, and (5) ethical and regulatory frameworks for human enhancement. Emerging trends include transformer-based architectures, self-supervised learning, federated learning for privacy-preserving applications, and the convergence of neurotechnology with IoT-enabled wearable systems. This survey provides a comprehensive mapping of the intellectual landscape, identifies persistent research gaps including clinical translation barriers and dataset standardization, and proposes future directions for responsible neural augmentation technology development.