Sensitivity of technology adoption to chosen modelling parameters
Whilst statistical approaches are well-suited to detecting underlying correlations in historical and experimental datasets, this on it's own does not provide a detailed understanding of the causation behind associated events. Equally, statistical methods are not generally well suited to predicting disruptive events and complex interactions, with other simulation techniques such as System Dynamics and Agent Based Modelling performing better in these areas. Accordingly, in order to identify causation effects and test the sensitivity of technological substitution patterns to variability arising from real-world socio-technical features not captured in simple bibliometric indicators (such as the influence of competition and economic effects), the fitted regression model is evaluated in a real-time system dynamics environment.
- 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?