  The aim of this paper is to develop and test metrics to quantitatively
identify technological discontinuities in a knowledge network. We developed
five metrics based on innovation theories and tested the metrics by a
simulation model-based knowledge network and hypothetically designed
discontinuity. The designed discontinuity is modeled as a node which combines
two different knowledge streams and whose knowledge is dominantly persistent in
the knowledge network. The performances of the proposed metrics were evaluated
by how well the metrics can distinguish the designed discontinuity from other
nodes on the knowledge network. The simulation results show that the
persistence times # of converging main paths provides the best performance in
identifying the designed discontinuity: the designed discontinuity was
identified as one of the top 3 patents with 96~99% probability by Metric 5 and
it is, according to the size of a domain, 12~34% better than the performance of
the second best metric. Beyond the simulation analysis, we tested the metrics
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 the qualitative studies, and the metrics were tested by
how well the metrics identify the selected patents as top-ranked patents. The
empirical results fully support the simulation results and therefore the
persistence times # of converging main paths is recommended for identifying
technological discontinuities for any technology.
