• Preliminary adoption data appears  to show a distinction between those technologies arising as a result of  technological failure, and those arising based on a presumptive technological  leap (to be confirmed)
  • From available patent data  indicators ‘cited patents’ and ‘cited references’ do seem to be able to provide a means of determining  the mode of adoption during the emergence phase of the Technology Life Cycle –  these two indicators are normally taken to correspond to the rates of  technological and scientific progress respectively in a given field
  • Patent indicator subset selected  for use in model building based on ranking exercise does appear to provide the  basis for a statistically significant technology classification model
  • Functional data analysis appears to  provide valid method to build a technology classification model based on  specific Technology Life Cycle stages
  • High-dimensional functional model  found to have the highest significance based on F-ratio statistics comparison
  • Permutation testing of the  functional linear regression analysis also suggests that the model built is  sensitive to the order of the technology time series being considered  (particularly in the high-dimensional case), so this relationship would appear  to be based on the specifics of the individual technology curves considered,  and does not appear to be occurring by chance
  • Comparison of functional linear  regression vs. functional principal components analysis: conclusions?