Identifying the mode and impact of technological substitutions
: Historical influences and evolutionary patterns

  • Ian Marr

Student thesis: Doctoral ThesisEngineering Doctorate (EngD)


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 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. This issue is exacerbated for organisations with 20 to 30 year technology development cycles, such as in the aerospace sector, where it could take many years of resource commitment to observe and fully understand development potential of new technologies. With little available data, being able to identify at an early stage whether new technologies are appearing in response to a perceived stagnation in technical developments (potentially signalling a rapid change about to take place), or as a result of pioneering leaps of scientific foresight (potentially signalling the need for a longer development cycle), poses a significant challenge.

This research combines bibliometric, pattern recognition, and statistical approaches with data-driven simulations to develop technology classification and substitution models from historical datasets where literature evidence supports mode labelling. The resulting functional regression classification 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, a system dynamics model enables the impact and causal influences of different substitution modes to be explored. Lastly, preliminary evidence suggests classification and forecasting can be achieved based on partial time series. This implies that future extensions to real-time applications may be possible for use in early stages of research and development. This capability would reduce uncertainty in decision-making, and consequently, time-to-market, enabling robust product/service strategies to be developed in response to continually evolving markets.
Date of Award25 Sep 2018
Original languageEnglish
Awarding Institution
  • The University of Bristol
SupervisorMark H Lowenberg (Supervisor) & McMahon Chris (Supervisor)

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