Abstract
Technological substitutions play a major role in the research and development efforts of most modern industries. If timed and provisioned well, successful technology substitutions can provide significant market advantages to firms that have anticipated the demand correctly for emergent technologies. Conversely, failure to commit to new technologies at the right time can have catastrophic consequences, making determining the likely substitution mode of critical strategic importance. With little available data, being able to identify at an early stage whether new technologies are appearing in response to perceived stagnation in existing technical developments, or as a result of pioneering leaps of scientific foresight, poses a significant challenge.
This paper combines bibliometric, pattern recognition, statistical, and data-driven approaches to develop a technology classification model from historical datasets where literature evidence supports mode labelling. The resulting functional linear regression model demonstrates robust predictive capabilities for the technologies considered, supporting the literature-based substitution framework applied, and providing evidence suggesting substitution modes can be recognised through automated processing of patent data. Further, preliminary evidence suggests that classification can be achieved based on partial time series, implying that future extensions to real-time classifications may be possible for decision-making in the early stages of research and development.
Keywords:
Technological substitutions, Patent bibliometrics, Pattern recognition, Classification, Technology Life Cycle, Emergence
Introduction
The introduction of new technologies into heavily regulated industries such as aerospace is often a very complex, time-consuming and expensive challenge that requires significant levels of research and development in order to ensure a successful technology substitution. This challenge is exacerbated when new technology options represent a fundamental shift away from well-established principles, as the risk and uncertainties involved increase significantly. This is currently the case in the anticipated transition from conventional turbojet aircraft architectures to all new electric configurations, and equally for the adoption of technologies enabling mass manufacturing and customisation processes in aerospace production lines. At the same time, the opportunities associated with these innovations may be sufficient to warrant decision-makers adopting new technological approaches. In some cases, new technologies arise even while existing technologies are still undergoing further developments, and have not yet reached the peak of their performance. This further complicates the decision for enterprises, as devoting significant resources to a new technological approach that may or may not out-perform the old one presents great commercial risk. In this regard it is beneficial to be able to identify early on whether a new technology is likely to have scope for development beyond that of the current dominant technology, and commercially, when the tipping point might occur where the new approach would become the industry ‘mainstream’ technology option.
This paper examines historical cases where emerging technologies have been presumed in advance to have development opportunities beyond those of pre-existing technologies, subsequently leading to transitions occurring before performance of the existing technology has stagnated. Based on conceptual models published previously that consider the mode of technological substitution and the relation to both scientific and technological developments, this paper looks to test whether separate bibliometric measures of scientific and technological development can be combined to provide an indication of the mode of adoption likely to occur from patent data available during the early stages of development. Bibliometric, pattern recognition, statistical and other data-driven analysis techniques are applied to technologies identified as having been adopted as a result of either prior technological stagnation (which we term technological failure with reactive substitution), or as a result of a presumptive leap being made, in order to identify early indicators of the mode of technological substitution. In the case of substitutions as a result of a presumptive leap, some forthcoming technical limit is recognised that prompts a transition before the current technology has stagnated. This historical classification has led to the development of a functional linear regression model that can be used in supporting technology strategy and innovation management by indicating the likely mode of adoption from key technology development indicators. In doing so, this paper has found good evidence in historical records to support the literature based categorisation into reactive and presumptive modes of substitution, and demonstrated that these modes can be recognised through automated processing of patent data. Preliminary evidence is also provided that suggests it may be possible to use partially complete datasets (i.e. segmented time series) to predict the end mode of substitution, potentially enabling future extensions to real-time applications. The paper begins by providing some background to technology substitutions and patent-based analysis techniques in section \ref{447134}, followed by an overview of bibliometric data sources, statistical analysis, and method selection in section \ref{274383}. Details of the derivation of the technology classification model using statistical ranking and functional data analysis are then provided in section \ref{806047}, along with the corresponding results and discussions in section \ref{559459}. Finally, conclusions from the patent indicator ranking and classification model building exercises are then summarised in section \ref{675825}.
Background
Technological substitution often plays an important role in the fortunes of enterprises. As such, numerous studies have previously examined the many complex factors that influence technology development and adoption trends. An overview of the relationships between technological performance, human perceived limits of science and technology, observed substitution patterns and behaviours, and patent-based forecasting techniques are provided here to explain the analysis that follows.
Technology forecasting, substitution patterns, and technological failure
Correctly predicting which emerging technologies are likely to be most influential can ensure that a firm is best positioned to gain a large advance over their competitors when the new technology comes to fruition. Conversely, failure to anticipate the arrival of big technological shifts can leave firms severely diminished. This is illustrated by the dramatic impact on Kodak's business following the introduction of digital photography, that rendered many of the firm's existing film products obsolete following an early lead in the digital field that was not fully capitalised upon \citep{Lucas_2009}. Equally, investing heavily in a nascent technology too soon can have grave consequences, as Bertlesmann found from investing in Napster \cite{Hall_2006}. As such, forecasting techniques are often used to determine strategies in large organisations by providing an initial guide to future opportunities, risks, challenges, & areas of uncertainty \cite{Daim_2006}.
In this field, considerable work has already been undertaken on the modelling of technology diffusion as part of these substitution events. This has included, amongst many other areas of study (see \cite{Peres_2010}), the influence of successive technology generations, and the impact of time delays on the perception of new technologies (see \cite{Bass_2004} and \cite{Datt_e_2007} respectively). Classically, the introduction of new technologies is often described as following an S-curve that assumes uptake is initially slow in the earliest stages, until performance and functional benefits of the new technology are seen to be greater than those of existing technologies, at which point uptake significantly accelerates \cite{richard1986innovation,utterback1994mastering}. This model assumes that eventually all technologies then arrive, driven by research and development efforts, at an ultimate limiting condition that is based on physical constraints, where performance improvements stagnate once again. However, in reality, periods of performance stagnation can also occur when challenging technical obstacles appear, or when market uptake slows (potentially due to market saturation, regulatory changes, or competition from new technologies), reducing investment in research and development \cite{myers1969successful,Poolton_1998}. This results in substitutions to the next generation of technologies occurring either before or after arriving at a perceived performance limit, which may or may not be an actual, or ultimate, performance limit \cite{Adner_2015,hughes1983networks}.
This brings about the notion of continual technological (or functional) failure, at the point where a replacement technology is sought for a currently stalled technological paradigm \cite{Sood_2005}. However, the technological 'failures' that lead to this reactive type of substitution vary greatly, and cannot just assume a single simple definition. In this regard, previous work has examined what is meant by 'technological failure', and has broadly categorised these occurrences as outlined in the work of Gooday \cite{Gooday_1998}. In the analysis that follows, this study focuses on failures relating to the ever more demanding expectations that human users impose on their technologies. Specifically, the definition of technological failure used in this study is given as:
“A point in time at which technology performance development stagnates/plateaus, with no further progressive trajectory improvements foreseen for a significant period of time in comparison to the overall technology lifecycle considered, which is subsequently followed by the substitution of a new technology/architecture that is on a progressive trajectory”
This means that a technology has been able to reach what could be observed to be a temporary performance limit in this condition before substitution to a new discontinuous technology occurs \cite{Schilling_2009}. This definition also follows on from the work of Sood & Tellis which applied a sub-sampling approach to analyse different types of 'multiple S-curves', and subsequently concluded that technologies tend to follow more of a step-function, with long periods of static performance interspersed with abrupt jumps in performance, rather than a classical S shape. In this study, stagnation periods were recorded where technology performance during a given sub-sample had an upper plateau longer in duration than the immediately preceding growth phase, whilst the subsequent jump in performance in the year immediately after the plateau was almost double the performance gained during the entire plateau \cite{Sood_2005}.
Anomalies associated with scientific and technological crisis
Up till now, only substitution patterns associated with technological failure have been discussed. However, previous studies have identified that technological substitutions are not just the result of the existing technology being deemed to have 'failed'. In this sense Edward Constant argued that a feature common to all technological revolutions was the emergence of 'technological anomalies', which could be traced to either scientific or technological crisis \cite{II_1973}. In the work of Constant the first, and most common, cause of these technological anomalies was attributed to functional failure. Conversely, technological anomalies were also identified as arising as a result of presumptive technological leaps:
"The demarcation between functional-failure anomaly and presumptive anomaly is that presumptive anomaly is deduced from science before a new paradigm is formulated and that scientific deduction is the sole reason for the sole guide to new paradigm creation. No functional failure exists; an anomaly is presumed to exist, hence presumptive anomaly" \cite{II_1973}
The type of crisis that emerges is dependent on which type of anomaly precedes it. Scientific crisis can occur irrespective of whether an alternative theoretical framework exists or not when a persistent, unresolved, scientific anomaly successfully refutes an established theory. In this condition the crisis is directly linked to the anomaly. However, technological anomaly and crisis are rarely so logically driven, and can arise in conditions where existing technological paradigms are still performing favourably. This is illustrated by the turbojet revolution of the 1930s and 1940s, where piston-engine developments provided remarkable performance improvements and continuing success, but were superseded by scientific predictions of a performance limit arising from propeller compressibility effects. Consequently scientific foresight was directly responsible for the radical technological changes that followed. In addition, in order for a technological anomaly to provoke a technological crisis, a convincing alternative paradigm must exist, so that the relative functional failure of the conventional system is observable. As such, the alternative technological paradigm instigates the crisis, whilst the technological anomaly may only be seen as speculation or as a limiting condition to the normal technology \cite{II_1973}.
Modes of substitution
Building on the works of Constant, Schilling, and Sood, a conceptual framework for analysing technology substitutions was published by Ron Adner that considers both the emergence challenges facing new technologies and the extension opportunities still available to existing technologies \cite{Adner_2015}. In this, four substitution regimes are proposed considering low and high scenarios for both new technology emergence challenges and old technology extension opportunities, and are demonstrated in the context of developments in semiconductor lithography equipment. These regimes are characterised as 1) Creative Destruction (low extension opportunity and low emergence challenge), 2) Robust Coexistence (high extension opportunity and low emergence challenge), 3) Resilience Illusion (low extension opportunity and high emergence challenge), and 4) Robust Resilience (high extension opportunity and high emergence challenge). Based on the definitions of functional failure and presumptive anomaly described in sections \ref{677399} and \ref{275337}, reactive technology substitutions correspond to quadrants 1 and 3 in Adner's substitution framework (i.e. substitutions based on low extension opportunities for existing technologies), whilst presumptive technology substitutions correspond to quadrants 2 and 4 (i.e. substitutions where there still appears to be high extension opportunities for existing technologies). Further details and examples of these technological substitution regimes are provided in \cite{Adner_2015} along with a review of the corresponding technology adoption S-curves.
The current study only considers the extension opportunity dimension in its classification of substitution modes in order to facilitate the development of the data-driven methodology presented here. It is worth noting that this analysis could be repeated and decomposed further into the four higher fidelity regimes proposed by Adner, but this would require additional case studies to ensure a sufficient number of technologies are available in each category, whilst also requiring supplementary literature or expert evidence to support category assignments. For this reason this study only considers the ability to distinguish between the two broader extension opportunity driven modes of substitution (i.e. reactive or presumptive) from analysis of historical scientific and technological data. Whilst the higher level modes considered here are characterised by the low and high extension opportunity scenarios respectively at the tail end of the existing technology's S-curve, variability in the emergence challenge dimension is assumed to slow the development of the new technology at the start of the subsequent S-curve. As such, this varies the initial curvature of the new technology's S-curve, rather than shifting in time the point of first emergence (which for this analysis is effectively treated as a static point). In terms of performance trends this means that a reactive substitution corresponds to a period of performance stagnation prior to the new technology first appearing, whilst a presumptive substitution corresponds to the new technology first emerging as the existing technology continues to improve. This is illustrated in Fig. \ref{398449}.