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The aim of this paper is to develop and test metrics to quantitativelyidentify technological discontinuities in a knowledge network. We developedfive metrics based on innovation theories and tested the metrics by asimulation model-based knowledge network and hypothetically designeddiscontinuity. The designed discontinuity is modeled as a node which combinestwo different knowledge streams and whose knowledge is dominantly persistent inthe knowledge network. The performances of the proposed metrics were evaluatedby how well the metrics can distinguish the designed discontinuity from othernodes on the knowledge network. The simulation results show that thepersistence times # of converging main paths provides the best performance inidentifying the designed discontinuity: the designed discontinuity wasidentified as one of the top 3 patents with 96~99% probability by Metric 5 andit is, according to the size of a domain, 12~34% better than the performance ofthe second best metric. Beyond the simulation analysis, we tested the metricsusing a patent set representative of the Magnetic information storage domain.The three representative patents associated with a well-known breakthroughtechnology in the domain, the giant magneto-resistance (GMR) spin valve sensor,were selected based on the qualitative studies, and the metrics were tested byhow well the metrics identify the selected patents as top-ranked patents. Theempirical results fully support the simulation results and therefore thepersistence times # of converging main paths is recommended for identifyingtechnological discontinuities for any technology.

Quantitative identification of technological discontinuities using simulation modeling