Data-Driven Insights into the Industrial Transformation Literature

This paper addresses the burgeoning challenge of navigating the expansive literature, particularly within industrial transformation and innovation. Given the multidisciplinary nature of this research area, which spans technological, economic, and organizational studies, the volume of relevant publications has grown significantly, necessitating efficient literature review methodologies. In response, the authors advocate for a no-code text mining approach that leverages word embedding, cosine distance calculations, complete linkage hierarchical clustering, and rapid automatic keyword extraction. This methodology is applied to a dataset comprising of 2.742 peer-reviewed journal articles from Scopus, focusing on their abstracts, keywords, and titles as the corpus. Through this approach, the paper systematically dissects the prevailing discourse, identifying key thematic clusters that encapsulate the current research landscape's methodological, technological, security-related, and business-oriented dimensions. The authors highlight a significant emphasis on sustainability, underscoring the integral role of digital technologies in fostering environmental stewardship alongside industrial innovation.