Emerging technology identification based on a dynamic framework: a lifecycle evolution perspective

Emerging technology plays a crucial role in promoting the development of the industrial economy, and identifying potential emerging technology is significant for innovation strategy. This study aimed to propose an identifying framework that can match the different stages of emerging technology, while most prior studies adopted a static method. Taking high-speed railway track (HSRT) technology as a case study, this paper presents an empirical application based on the growth model of the technology lifecycle, artificial neural network, and ordinal regression. The result indicates that the emerging technology branches of the HSRT technology include B66, C04, B28, and C08 represented by IPC categories, among which B66 is the most prominent. Besides, this study concluded that explosiveness, persistence, breakthrough, and competitive advantage in technology development are the typical characteristics for identifying emerging technology. The contribution of this study is enlightening a dynamic view of technology identification, which promotes the understanding of emerging technology by applying and integrating quantitative methods.