Exploring Patterns of Academic-Industrial Collaboration for Digital Transformation Research: A Bibliometric-Enhanced Topic Modeling Method

Interactions between industry and academia provide inspiration for knowledge fusion, and more importantly serve as a stimulus for both basic and applied research, creating impact and potential opportunities. From the perspective of text mining and co-authorship network analysis, this paper aims to explore the patterns of academic-industrial collaboration using publication data. We propose a bibliometric-enhanced topic modeling method to profile the core constituents of industry and academia collaborative hotspots in digital transformation research, using a 10-year publication dataset from 2009 to 2018 extracted from Web of Science. We then examine interactions and distinctions between topics authored by only academic researchers and having industrial collaboration to further develop a comprehensive understanding of the content and driving force of industrial engagement. The empirical insights of this paper provide a detailed picture of academic-industrial linkages, which potentially can be used to lead academics to engage with industry, and assist innovation management and problem-solving in digital transformation research and practice.