Digital Transformation Strategies, Practices, and Trends: A Large-Scale Retrospective Study Based on Machine Learning

Fatih Gurcan, Gizem Dilan Boztas, Gonca Gokce Menekse Dalveren and Mohammad Derawi ()
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Fatih Gurcan: Department of Management Information Systems, Faculty of Economics and Administrative Sciences, Karadeniz Technical University, 61080 Trabzon, Turkey
Gizem Dilan Boztas: Digital Transformation Office, Karadeniz Technical University, 61080 Trabzon, Turkey
Gonca Gokce Menekse Dalveren: Department of Software Engineering, Faculty of Engineering, Atilim University, 06830 Ankara, Turkey
Mohammad Derawi: Department of Electronic Systems, Faculty of Information Technology and Electrical Engineering, Norwegian University of Science and Technology, 7034 Gjøvik, Norway

Sustainability, 2023, vol. 15, issue 9, 1-23

Abstract: The purpose of this research is to identify the areas of interest, research topics, and application areas that reflect the research nature of digital transformation (DT), as well as the strategies, practices, and trends of DT. To accomplish this, the Latent Dirichlet allocation algorithm, a probabilistic topic modeling technique, was applied to 5350 peer-reviewed journal articles on DT published in the last ten years, from 2013 to 2022. The analysis resulted in the discovery of 34 topics. These topics were classified, and a systematic taxonomy for DT was presented, including four sub-categories: implementation, technology, process, and human. As a result of time-based trend analysis, “Sustainable Energy”, “DT in Health”, “E-Government”, “DT in Education”, and “Supply Chain” emerged as top topics with an increasing trend. Our findings indicate that research interests are focused on specific applications of digital transformation in industrial and public settings. Based on our findings, we anticipate that the next phase of DT research and practice will concentrate on specific DT applications in government, health, education, and economics. “Sustainable Energy” and “Supply Chain” have been identified as the most prominent topics in current DT processes and applications. This study can help researchers and practitioners in the field by providing insights and implications about the evolution and applications of DT. Our findings are intended to serve as a guide for DT in understanding current research gaps and potential future research topics.

Keywords: digital transformation; trends and practices; topic modeling; retrospective analysis (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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