Quantitative Identification of Technological Discontinuities

The aim of this paper is to develop and test metrics to quantitatively identify technological discontinuities in a knowledge network. We adopted and developed four metrics based on innovation theories and tested the metrics by using a patent set representative of the Magnetic information storage domain. The three representative patents associated with a well-known breakthrough technology in the domain, the giant magneto-resistance (GMR) spin valve sensor, were selected based on qualitative studies, and the metrics were tested by how well each identifies the selected patents as top-ranked patents. The empirical results show that, first, global citation structure-based metrics clearly provide better performance in identification of technological discontinuities than local citation count-based metrics, second, non-continuous nodes on the major knowledge networks are not at all related to technological discontinuities, and, third, the two global metrics (Metric2: z-score of Persistence and Metric 4: z-score of Persistence times # of converging main paths) successfully identified the three selected patents as top-ranked patents out of over 30,000 patents, therefore, Metric 2 and 4 are recommended for identifying and quantifying technological discontinuities for any technology