This implies that it is possible to predict modes of substitution from limited bibliometric data during the earliest stages of technology development, but equally shows that the functional data approach used corroborates well the earlier statistical rankings produced using Dynamic Time Warping, K-Medoids clustering, and leave-one-out cross-validation.
Whilst it has not been possible to detail all elements of this analysis
Regression models can be very fickle to the datasets they are calibrated to - risk of over-fitting - attempted to compensate for this using the leave-one-out cross validation, but this does not guarantee generalisation to all technologies.
Limited number of technologies considered (cross-validation would benefit from a more diverse group of technologies - see section \ref{258858})
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 presented here needs to be evaluated in a causal environment (such as by modelling using real-time system dynamics).