The results obtained from this analysis suggest that statistical analysis of patent indicator time series that have been segmented based on Technology Life Cycle features provides a possible means for classification of technological substitutions. Specifically, for the datasets considered measures of the number of cited references and the involvement of non-corporate entities by year during the emergence phase were found to provide a good indication of the expected mode of substitution when used as a basis for functional linear regression (correctly classifying 19 out of 20 technologies), and performed consistently well in statistical ranking of predictive capability. These patent data dimensions can in turn be associated with technological and scientific production respectively, in line with the predictions made about requirements for demonstrating the conditions for presumptive technological substitutions based on the work of Constant in section \ref{585124}. Whilst this pairing of patent indicators demonstrated the most robust out-of-sample performance of any of the patent indicator subsets considered as mode predictors when basing analysis on the emergence stage, this does not prove that these are the only indicators capable of doing so. As discussed in section \ref{311620}, the possibility of orthogonality has not been ruled out with regards to the other patent indicators shown in Table \ref{table:bibliometric_indicators}. However, these two dimensions are in good agreement with the technological anomaly arguments put forward by Constant, and so were felt to be reasonable for forming the basis of the technology classification model that has been developed.
based on the two principal classes considered from literature evidence
based on the performance-based modes of substitution outlined in section

Drawing from the ideas of Constant and preceding technological substitution studies this paper has attempted to explore the possibility of classification of two principle modes of substitution relationships between 
Whilst it has not been possible to detail all elements of this analysis
It was postulated in  section 
Technology classification model is built on the assumptions given in section \ref{585124}.
In this regard the technology classification system is based on technology profile dimensions relating to both scientific and technological progress.
If both of these components are missing, the functional linear regression model defaults to a prediction of technological substitution by functional failure (i.e. there does not appear to be any opportunity for adoption).
Limited number of technologies considered (cross-validation would benefit from a more diverse group of technologies - see section \ref{258858})
Two principal modes of technology substitution examined from literature and technology adoption case studies: reactive and presumptive

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.