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Digital transformation has recently introduced itself as a groundbreaking phenomenon with profound impacts on societies, industries, businesses, and even individuals. Accordingly, several studies have attempted to give a literature review or analysis of digital transformation research during the last few years. However, most of them are domain-specific studies based on small data samples or subjective review methods, so we lack a general and robust understanding of the landscape of this field of research across different disciplines and domains. Taking a step toward filling this gap, the current study aims to shape an overall and reliable picture of the research realm on digital transformation. To the aim, a computational method namely topic modeling was applied to two big texts, one of which includes all digital transformation-related publications that were indexed in well-known Scopus and Web of Science databases (8639 documents), and the other one only contains studies that were published by high-quality JCR journals (1264 documents). As a result, 20 and 13 topics were respectively introduced as the underlying themes of the global trends and core knowledge in digital transformation research along with their temporal evolutionary paths throughout the recent years. Also, by comparing these two groups of topics, it was known that there are nine developing trends in this field of research that require more attention and advancements to establish themselves as the core knowledge of the field. Complementing the contributions of previous domain-specific or subjective reviews on digital transformation, this study tries to favor a better understanding of this scholarship through multidisciplinary and multidimensional analyses of digital transformation-related publications by using the topic modeling approach. [ABSTRACT FROM AUTHOR]