RT Book, Section
A1 Volpentesta, Tiziano
T1 Exploring Digital Transformation and Artificial Intelligence (AI) in Management: A State-of-the-Art Assessment Through AI
T2 The Digital Transformation of Organizations
A2 Volpentesta, Tiziano
YR 2026
DO 10.1108/978-1-80592-096-020261003
SP 0
SN 978-1-80592-097-7
PB Emerald Publishing Limited
AB This chapter offers a large-scale, data-driven assessment of the research landscape at the intersection of digital transformation (DT) and artificial intelligence (AI). While both domains have grown exponentially in the past decade, the rapid accumulation of knowledge has led to fragmentation across disciplines and perspectives. To address this, the authors employ a hybrid methodological approach that integrates systematic literature review with computational text analysis. Drawing from over 30,000 academic articles retrieved from Scopus, the authors’ analysis combines traditional bibliographic rigor with advanced natural language processing techniques based on transformer architectures. Using BERTopic, a state-of-the-art topic modeling algorithm, the authors identify and visualize the thematic structures underpinning the DT and AI literatures. Topic maps reveal the presence of distinct yet interconnected clusters, including themes of organizational agility, digital business models, sustainability, and AI ethics. Dynamic topic modeling further uncovers how research priorities evolved from 2000 to 2024, highlighting convergence around transformative phenomena such as generative AI and postpandemic digital adoption. Comparative analysis between the AI and DT corpora shows that while AI research increasingly addresses sustainability and workforce implications, DT studies focus more on sectoral implementations, healthcare, and inter-organizational collaboration. This chapter advances both a methodological and conceptual contribution: it demonstrates how computational approaches can enhance theory-building through large-scale synthesis and reveals how AI and DT have co-evolved as intertwined yet distinct pillars of contemporary organizational research.
RD 4/10/2026
UL https://doi.org/10.1108/978-1-80592-096-020261003