- 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?