The topic of technology development and its disruptive effects has been the subject of much debate over the last 20 years with numerous theories at both macro and micro scales offering potential models of technology progression and disruption. This paper focuses on how theories of technology progression may be integrated and considers whether suitable indicators of this progression and any subsequent disruptive effects might be derived, based on the use of big data analytic techniques. Given the magnitude of the economic, social, and political implications of many disruptive technologies, the ability to quantify disruptive change at the earliest possible stage could deliver major returns by reducing uncertainty, assisting public policy intervention, and managing the technology transition through disruption into deployment. However, determining when this stage has been reached is problematic because small random effects in the timing, direction of development, the availability of essential supportive technologies or “platform” technologies, market response or government policy can all result in failure of a technology, its form of adoption or optimality of implementation. This paper reviews key models of technology evolution and their disruptive effect including the geographical spread of disruption. The paper then describes a use case and an experiment in disruption prediction, looking at the geographical spread of disruption using internet derived historic data. The experiment, although limited to one specific aspect of the integrated model outlined in the paper, provides an initial example of the type of analysis envisaged. This example offers a glimpse into the potential indicators and how they might be used to measure disruption hinting at what might be possible using big data approaches.
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