A novel integrated approach for quantifying the convergence of disruptive technologies from science to technology

With rapid developments in science and technology, knowledge transfer from science to technology and technology convergence from different fields are accelerating. Technology convergence has become a main source of disruptive technologies (DTs). To facilitate enterprise R&D strategic decision-making and government innovation policies formulation, it is necessary to quantify the convergence processes of DTs and understand the DTs' emergence characteristics from science to technology. Existing research on technology convergence measurement mainly used patent citation information, patent co-classification analysis, and text mining. However, since these studies have limited analysis of the sources and causes of technology convergence from the perspective of knowledge memes, resulting in insufficient revelation of the processes and characteristics of DTs' emergence. Knowledge meme theory helps to reveal the relationships between knowledge diffusion, knowledge convergence, and technology convergence. Therefore, in this paper, we proposed a research framework for quantifying the convergence of DTs from science to technology. In this framework, we analyzed the knowledge diffusion and technology convergence of DTs from science to technology based on knowledge meme theory. We also integrated patent citation analysis, text mining, and cascade network models to quantitatively measure knowledge diffusion and technology convergence characteristics. We tried to understand the generation mechanisms of DTs from the perspective of technology convergence. We took smartphones as a case study to verify the framework's validity and flexibility. This paper provides a novel approach for quantifying the convergence of DTs from science to technology, which can help us to understand the emergence and development trends of DTs. This paper will also be of interest to smartphone technology R&D experts.