Understanding the evolution of an emerging technological paradigm and its impact: The case of Digital Twin

The interest of the academic and practitioner communities on the topic of Digital Twin has grown substantially in recent years. Bibliometric analysis can serve as a useful tool to explore the roadmap of the Digital Twin across various emergent themes over time. In this paper, we compare and analyze 1270 news articles and 4036 research publications to assess the evolution of the Digital Twin paradigm according to these sources from 2016 to 2021. We apply topic modeling and sentiment analysis on the textual corpora. Our analysis shows that certain topics related to applications, simulation, and enabling technologies for Digital Twin find greater coverage and generate higher positivity over time. We ascertain the coevolution and divergence in the number and sentiment of topics through curve matching metrics and determine whether they can rouse consumer interest, captured through online search trends. Our regression analysis shows that news on applications of Digital Twin and research on process evaluation through real-time simulation significantly impact the search frequency of consumers. Our research helps the digital product and service providers to understand the academia-industry gap in their effort to investigate Digital Twin and guides them on steps to take and themes to pursue for generating consumer interest. • We conduct bibliometric analysis of 1270 news articles and 4036 research publications on Digital Twin. • We use topic modeling to identify the evolution of topics for Digital Twin from 2016 to 2021. • We examine the change in sentiments generated by the topics on Digital Twin over time. • We determine the consumer interest on Digital Twin through search queries on Google Trends. • We use regression analysis to ascertain the impact of news and research on consumer search.