Brackin, R.C.; Jackson, M.J.; Leyshon, A.; Morley, J.G. Taming Disruption? Pervasive Data Analytics, Uncertainty and Policy Intervention in Disruptive Technology and its Geographic Spread. ISPRS Int. J. Geo-Inf.2019, 8, 34.
Brackin, R.C.; Jackson, M.J.; Leyshon, A.; Morley, J.G. Taming Disruption? Pervasive Data Analytics, Uncertainty and Policy Intervention in Disruptive Technology and its Geographic Spread. ISPRS Int. J. Geo-Inf. 2019, 8, 34.
Brackin, R.C.; Jackson, M.J.; Leyshon, A.; Morley, J.G. Taming Disruption? Pervasive Data Analytics, Uncertainty and Policy Intervention in Disruptive Technology and its Geographic Spread. ISPRS Int. J. Geo-Inf.2019, 8, 34.
Brackin, R.C.; Jackson, M.J.; Leyshon, A.; Morley, J.G. Taming Disruption? Pervasive Data Analytics, Uncertainty and Policy Intervention in Disruptive Technology and its Geographic Spread. ISPRS Int. J. Geo-Inf. 2019, 8, 34.
Abstract
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 scale offering potential models of technology progression and disruption. This paper focuses on how the potential theories of technology progression can be integrated and considers whether suitable indicators of this progression and any subsequent disruptive effects (particularly considering these geographically) 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 some of the key models of technology evolution and their disruptive effect including, in particular, the geographical spread of disruption. It suggests a methodology for utilising the recent explosion of open and web-discoverable data to determine a methodology to achieve this earlier determination and considers the potential exploitation of big data modelling and predictive analytical techniques to achieve this goal.
Keywords
geographies of disruption; data analytics; policy intervention; Uber; disruptive technology; disruptive innovation; path dependency; platform development; platform economics
Subject
Social Sciences, Geography, Planning and Development
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
