In addition to the modes of substitution outlined in Table \ref{table:technology_categories}, other technologies have been identified as 'non-starters': these are technologies that were never mass commercialised. In many cases these technologies could have been adapted for the target markets considered but were either never used or failed to demonstrate the required features, or performance and cost improvements necessary to warrant further development beyond initial trials. Examples of non-starter technologies include wire recorders as an alternative to magnetic tape technology and chain printers as an alternative to dot matrix printers. In the case of wire recorders, this format failed to take-off after it was excluded from the standard-setting process in favour of magnetic tape technology, leading to “technological lock-out”, whilst early chain printers were quickly eclipsed by the superior performance of the dot matrix design. Non-starters are excluded in this study, as the analysis that follows is based on classifying individual technologies based on technologies that are known to have been successfully commercialised, and as such it is not believed their inclusion would influence the results presented here, although non-starters would need to be included for reducing uncertainty in the classification of emerging technologies \cite{Sood_2005}.
Based on Constant's hypothesis regarding scientific and technological anomalies and their influence on the mode of technological substitution, this paper looks to test whether bibliometric measures of scientific and technological development can provide an indication of the mode of adoption likely to occur. Consequently, this study theorises that in order to identify cases of technological substitution arising from presumptive anomaly a classification scheme would need to consider:
- a population’s perception of the current rate of scientific development in observed domains \cite{II_1973}
- a population’s perception of the current rate of technological development in observed domains \cite{II_1973}
Measuring perceptions of limits of science and technology
Many indicators of science and technological progress have been developed in the fields of bibliometrics and scientometrics in recent decades. Whilst these have largely been developed for the purposes of identifying and targeting gaps in existing knowledge, as well as for determining the effectiveness of funding in specific fields of research, they also provide a systematic manner to compare development trends across a broad range of scientific domains. When attempting to measure science it is however important to ensure that any measurements taken are suitable indicators of the development characteristics that are being studied. In this regard conceptual distinctions exist between scientific activity, scientific production, and scientific progress \cite{Martin_1996}:
- Scientific activity: consumption of the inputs to basic research (e.g. related to the number of scientists involved, level of funding, support staff and equipment)
- Scientific production: extent to which consumption of resources creates a body of scientific results. Results are embodied both in research publications and in other types of less formal communication between scientists
- Scientific progress: extent to which scientific activity results in substantive contributions to scientific knowledge
Based on this, indicators of scientific progress, such as citation analysis, are normally considered most appropriate for assessing scientists' success in producing new scientific knowledge and for identifying emerging areas of development, leading to their common usage in the tenure review process \cite{Narin_1996}. At the same time, simple publication counts are considered to provide a reasonable measure of scientific production, but are thought to be much less adequate as an indicator of contributions to scientific progress due to the unclear value of each publications individual contribution to knowledge. Publication counts actually reflect both the level of scientific progress made by an individual or group, as well as a number of other factors relating to the social and political pressures behind a study (e.g. publication practices of the employing institution, country and research area, or emphasis placed on publications for obtaining promotion or grants) \cite{verbeek2002measuring,Martin_1996}. Realistically these other extraneous factors cannot be assumed to be small in comparison to the scientific claims made, or that these effects are randomly distributed and cancel out \cite{Martin_1996}. However in this study, the emphasis is not on assessing the performance or influence of a specific set of papers, but rather to gauge the adoption of the field as a whole. As technology diffusion models also rely on non-invested parties being made aware of scientific and technological progress, communication and promotion of scientific research are important factors to include in adoption processes \cite{Bass_2004}. Adoption is equally dependent on perceptions of current scientific and technological rates of progress (shaped by social and political pressures, as well as technical), rather than the actual rates of progress (shaped by technical contributions to knowledge). Lastly, diffusion effects are population size, word-of-mouth, and time dependent \cite{Bass_2004}. As a result, measures of scientific production are felt to be a more relevant indication of likelihood to adopt than measures of scientific progress, although they could also indicate a potentially contentious or controversial topic that is generating lots of different opinions. However, controversy does not necessarily prevent adoption, and in some cases may accelerate substitution mechanisms, so this is not believed to significantly skew the trends presented here in either direction away from the intended simplified reflection of real-world adoption characteristics.
Methodology
Statistical comparisons of time series
Using bibliometric analysis methods it is possible to extract a variety of historical trends for any technologies of interest, effectively generating a collection of time series data points associated with a given technology (these multidimensional time series datasets are referred to here as 'technology profiles'). This raises the question of how best to compare dissimilar bibliometric technology profiles in an unbiased manner in order to investigate whether literature based technology substitution groupings can be determined using a classification system built on the assumptions given in section \ref{585124}. In particular comparisons of technology time series can be subject to one or more areas of dissimilarity: time series may be based on different number of observations (e.g. covering different time spans), be out of phase with each other, may be subject to long-term and shorter term cyclic trends, be at different stages through the Technology Life Cycle (or be fluctuating between different stages) \cite{little1981strategic}, or be representative of dissimilar industries. As such, a body of work already exists on the statistical comparison of time series, and in particular time series classification methods \cite{lin2012pattern}. Most modern time series pattern recognition and classification techniques emerging from the machine learning and data science domains broadly fall within the categories of supervised, semi-supervised, or unsupervised learning approaches. The distinction between these categories is based on the amount of training information provided to the classifier in each case. In supervised learning, training time series are provided with known classification labels, whilst training time series with both known and unknown classification labels are used in semi-supervised learning. By contrast, unsupervised learning approaches are not provided with any classification labels, and as such are required to determine groupings independently (e.g. clustering) \cite{lin2012pattern}. Table \ref{table:time_series_pattern_recognition_techniques} provides an overview of time series pattern recognition techniques commonly used (this list is not exhaustive).