Digital transformation (DT) is prevalent in businesses today. However, current studies to guide DT are mostly qualitative, resulting in a strong call for quantitative evidence of exactly what DT is and the capabilities needed to enable it successfully. With the aim of filling the gaps, this paper presents a novel bibliometric framework that unearths clues from scientific articles and patents. The framework incorporates the scientific evolutionary pathways and hierarchical topic tree to quantitatively identify the DT research topics’ evolutionary patterns and hierarchies at play in DT research. Our results include a comprehensive definition of DT from the perspective of bibliometrics and a systematic categorization of the capabilities required to enable DT, distilled from over 10,179 academic papers on DT. To further yield practical insights on technological capabilities, the paper also includes a case study of 9,454 patents focusing on one of the emerging technologies – artificial intelligence (AI). We summarized the outcomes with a four-level AI capabilities model. The paper ends with a discussion on its contributions: presenting a quantitative account of the DT research, introducing a process-based understanding of DT, offering a list of major capabilities enabling DT, and drawing the attention of managers to be aware of capabilities needed when undertaking their DT journey.
Author(s): Mengjia Wu, Dilek Cetindamar Kozanoglu, Chao Min, Yi Zhang
Organization(s): University of Technology Sydney, Nanjing University
Source: Advanced Engineering Informatics
Artificial intelligence, as an emerging and multidisciplinary domain of research and innovation, has attracted growing attention in recent years. Delineating the domain composition of artificial intelligence is central to profiling and tracking its development and trajectories. This paper puts forward a bibliometric definition for artificial intelligence which can be readily applied, including by researchers, managers, and policy analysts. Our approach starts with benchmark records of artificial intelligence captured by using a core keyword and specialized journal search. We then extract candidate terms from high frequency keywords of benchmark records, refine keywords and complement with the subject category “artificial intelligence”. We assess our search approach by comparing it with other three recent search strategies of artificial intelligence, using a common source of articles from the Web of Science. Using this source, we then profile patterns of growth and international diffusion of scientific research in artificial intelligence in recent years, identify top research sponsors in funding artificial intelligence and demonstrate how diverse disciplines contribute to the multidisciplinary development of artificial intelligence. We conclude with implications for search strategy development and suggestions of lines for further research.
Link to FULL-TEXT https://link.springer.com/article/10.1007/s11192-021-03868-4
Author(s): Na Liu, Philip Shapira, Xiaoxu Yue
Organization(s):Shandong Technology and Business University, University of Manchester, Tsinghua University