This study examines the evolution and intellectual structure of research on sustainable digital transformation in business systems, focusing on key trends, thematic clusters, and emerging directions at the intersection of digitalization and sustainability. A bibliometric analysis was conducted using data extracted from the Scopus database for the period 2010–2025. After applying explicit filtering criteria related to document type, language, subject area, publication period, and topical relevance, a final dataset of publications was analyzed. VOSviewer was used to perform co-occurrence, co-citation, and keyword network analyses, enabling the identification of major research clusters and thematic linkages. The results indicate a rapidly expanding field structured around digital transformation strategies, sustainable business models, artificial intelligence applications, circular economy integration, and governance mechanisms supporting sustainability transitions. The analysis shows a gradual shift from technology-centered digitalization toward integrated frameworks combining innovation with environmental and social sustainability objectives. The study contributes by providing a systematic map of the knowledge structure of sustainable digital transformation and by consolidating fragmented research streams. The findings offer useful insights for managers, policymakers, and researchers interested in integrating digital technologies into resilient and sustainable business models.
Maria Loredana Popescu, Ion Mihai Troacă, Flavius Constantin Nedelcea et al.· New Trends in Sustainable Bu...· 0 citations
The rapid emergence of generative artificial intelligence (AI) is reshaping teaching, learning, and academic work, creating new opportunities and challenges for educational leadership. While recent studies have documented increasing AI adoption in higher education, existing evidence is largely derived from technologically advanced contexts and early adopters, offering limited insight into institutions where AI integration remains in its initial stages. Consequently, there is a need to better understand how emerging AI practices may influence future leadership strategies and human–AI collaboration in higher education.This exploratory study investigates the patterns of AI use among students and educators within an emerging adoption context. Drawing on survey data collected from 76 university students and 38 higher education educators from multiple institutions, the study examines the educational tasks supported by AI, perceived benefits and challenges, and expectations regarding the evolving role of AI in academic activities. Rather than measuring institutional AI maturity or leadership effectiveness, the study seeks to identify early signals of human–AI collaboration that may inform educational leadership in the context of gradual AI adoption.The findings indicate that AI is primarily employed to support routine educational activities, including information retrieval, content summarization, brainstorming, text improvement, lesson preparation, and administrative tasks. Both students and educators perceive AI predominantly as an augmentation tool that enhances productivity and facilitates learning rather than as a substitute for human expertise. However, concerns regarding academic integrity, critical thinking, overreliance on AI-generated content, digital competencies, and the absence of institutional policies remain significant barriers to broader adoption. These findings suggest that higher education institutions are currently experiencing an early phase of AI integration in which leadership priorities extend beyond technology implementation toward fostering responsible AI use, developing digital capabilities, and establishing governance mechanisms that support effective human–AI collaboration.The study contributes to the emerging literature on educational leadership by extending discussions of AI adoption beyond technologically mature environments and highlighting the importance of leadership preparedness during the early stages of organizational AI adoption. Rather than offering generalizable conclusions, the findings provide exploratory evidence that can support the development of future conceptual models and empirical research on educational leadership, human–AI collaboration, and institutional AI governance across diverse higher education contexts.
Bogdan Costache· International Journal of Edu...· 0 citations
The emergence of autonomous artificial intelligence agents represents a new phase in the evolution of generative artificial intelligence, extending AI capabilities beyond content generation toward autonomous reasoning, workflow orchestration, institutional coordination, and organizational decision support. Among these developments, Claude Agents exemplify a new generation of agentic AI systems capable of executing complex multi-step tasks, managing institutional information, and interacting continuously with human users across diverse educational environments. Although generative artificial intelligence has attracted substantial scholarly attention, research remains largely centered on pedagogical applications, while the governance and leadership implications of autonomous AI agents continue to be conceptually fragmented and insufficiently theorized.This paper develops an integrative conceptual framework that examines how Claude Agents may transform educational leadership, institutional governance, and strategic decision-making in schools and higher education institutions. Drawing upon interdisciplinary scholarship in educational leadership, organizational governance, socio-technical systems theory, organizational information processing theory, human-AI collaboration, and digital transformation, the study adopts a conceptual qualitative methodology based on an integrative literature review and theory-building approach. The analysis argues that Claude Agents should not be understood merely as intelligent assistants but as agentic organizational actors that increasingly participate in institutional information processing, policy implementation, strategic planning, administrative coordination, and evidence-informed decision-making. Their integration creates opportunities for more adaptive, transparent, and data-informed governance while simultaneously introducing new challenges related to accountability, explainability, algorithmic bias, professional autonomy, institutional legitimacy, and ethical oversight.Building on these insights, the paper proposes the Human-AI Governance Framework for Educational Leadership (HAGF), which conceptualizes leadership as a collaborative governance process in which human judgment and autonomous AI agents jointly contribute to organizational decision-making within clearly defined institutional, ethical, and regulatory boundaries. The study contributes to emerging debates on agentic artificial intelligence by extending existing theories of educational leadership beyond technology adoption toward a governance-oriented perspective that integrates organizational resilience, distributed intelligence, and responsible AI governance. Finally, the paper identifies a future research agenda focused on AI-enabled leadership, institutional trust, governance architectures, and the evolving relationship between educational leaders and autonomous intelligent agents.
Bogdan Costache· International Journal of Edu...· 0 citations
A systematic literature review and bibliometric analysis of the emerging research domain of agentic artificial intelligence in organizations reveals a significant shift from technical investigations of autonomous systems toward questions concerning organizational decision-making, human-agent collaboration, governance mechanisms, and the strategic implications of increasingly autonomous AI systems.
Bogdan Costache, V. Enǎchescu, Costin Petcu· International Journal of Edu...· 0 citations
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